Journal articles on the topic 'Diabetes Complication predictions'
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1
Schallmoser, Simon, Thomas Zueger, Mathias Kraus, Maytal Saar-Tsechansky, Christoph Stettler, and Stefan Feuerriegel. "Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study." Journal of Medical Internet Research 25 (February 27, 2023): e42181. http://dx.doi.org/10.2196/42181.
Abstract:
Background Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. Objective This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes. Methods In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values. Results Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications. Conclusions Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks.
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Haber, Philipp K., Christoph Maier, Anika Kästner, Linda Feldbrügge, Santiago Andres Ortiz Galindo, Dominik Geisel, Uli Fehrenbach, et al. "Predicting the Risk of Postoperative Complications in Patients Undergoing Minimally Invasive Resection of Primary Liver Tumors." Journal of Clinical Medicine 10, no. 4 (February 10, 2021): 685. http://dx.doi.org/10.3390/jcm10040685.
Abstract:
Minimal-invasive techniques are increasingly applied in clinical practice and have contributed towards improving postoperative outcomes. While comparing favorably with open surgery in terms of safety, the occurrence of severe complications remains a grave concern. To date, no objective predictive system has been established to guide clinicians in estimating complication risks as the relative contribution of general patient health, liver function and surgical parameters remain unclear. Here, we perform a single-center analysis of all consecutive patients undergoing laparoscopic liver resection for primary hepatic malignancies since 2010. Among the 210 patients identified, 32 developed major complications. Several independent predictors were identified through a multivariate analysis, defining a preoperative model: diabetes, history of previous hepatectomy, surgical approach, alanine aminotransferase levels and lesion entity. The addition of operative time and whether conversion was required significantly improved predictions and were thus incorporated into the postoperative model. Both models were able to identify patients with major complications with acceptable performance (area under the receiver-operating characteristic curve (AUC) for a preoperative model = 0.77 vs. postoperative model = 0.80). Internal validation was performed and confirmed the discriminatory ability of the models. An easily accessible online tool was deployed in order to estimate probabilities of severe complication without the need for manual calculation.
3
Tang, Baoyu, Yuyu Yuan, Jincui Yang, Lirong Qiu, Shasha Zhang, and Jinsheng Shi. "Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records." Sensors 22, no. 21 (November 3, 2022): 8454. http://dx.doi.org/10.3390/s22218454.
Abstract:
Diabetes is an increasingly common disease that poses an immense challenge to public health. Hyperglycemia is also a common complication in clinical patients in the intensive care unit, increasing the rate of infection and mortality. The accurate and real-time prediction of blood glucose concentrations after each short-acting insulin injection has great clinical significance and is the basis of all intelligent blood glucose control systems. Most previous prediction methods require long-term continuous blood glucose records from specific patients to train the prediction models, resulting in these methods not being used in clinical practice. In this study, we construct 13 deep neural networks with different architectures to atomically predict blood glucose concentrations after arbitrary independent insulin injections without requiring continuous historical records of any patient. Using our proposed models, the best root mean square error of the prediction results reaches 15.82 mg/dL, and 99.5% of the predictions are clinically acceptable, which is more accurate than previously proposed blood glucose prediction methods. Through the re-validation of the models, we demonstrate the clinical practicability and universal accuracy of our proposed prediction method.
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Alruwaytie, Wedad, Amal Mackawy, and Ali Abu Dahash. "Cystatin C and Fibrinogen Plasma Levels as early Predictors of Diabetic Nephropathy in Type II Diabetes Mellitus; a Review Article." Pakistan Journal of Medical and Health Sciences 16, no. 1 (January 30, 2022): 716–20. http://dx.doi.org/10.53350/pjmhs22161716.
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Background: Diabetic nephropathy (DN) is a dangerous illness associated with a significant risk for cardiovascular and kidney problems in diabetic patients. Serum cystatin C levels may rise in diabetic individuals with microalbuminuria, and it has been recommended as an endogenous glomerular filtration rate (GFR) marker because of its link to the albumin to creatinine ratio (ACR) in diabetic nephropathy. Uncontrolled diabetes was found to have a greater level of fibrinogen; in diabetic nephropathy, fibrinogen levels are important. In addition, fibrinogen has been associated to inflammation and has been demonstrated to play a crucial pathophysiologic role in the advancement of renal impairment in individuals. Methods: The author has no intention of commenting on the molecular role of cystatin C, a cysteine protease inhibitor, or the disrupted haemostatics mechanism in fibrinogen-induced diabetes. in this study, which is expected to investigate views on using the cystatin C as well as plasma fibrinogen plasma levels like an early markers of nephrotic syndrome. Therefore, 4 important clinical datasets were reviewed, including the EMBASE, PubMed and The Cochrane Library, Medline, Google Scholar and a few additional related journals datasets, as well as relevant records were collected with high precision. Conclusion: When compared to the frequently used creatinine-based predictions, cystatin C is a good marker for diagnosing nephropathy in patients with normal albuminuria, and it may enhance the risk prediction in diabetics. Even before the complication of chronic kidney disease symptoms, Cystatin C levels in urine might be raised in diabetic individuals. Additional investigation into cystatin C and fibrinogen functions as early biomarkers, clinical value in screening, involvement in prognosis, decrease of inflammation and prediction of medication clearance, and drug monitoring in type II diabetic, nephropathy is needed. Keywords: cystatin C, fibrinogen, nephropathy, diabetes mellitus, biomarkers
5
Zuo, Ming, Wei Zhang, Qi Xu, and Dehua Chen. "Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning." Journal of Healthcare Engineering 2022 (April 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/5129125.
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Objective. Diabetic complications have brought a tremendous burden for diabetic patients, but the problem of predicting diabetic complications is still unresolved. Our aim is to explore the relationship between hemoglobin A1C (HbA1c), insulin (INS), and glucose (GLU) and diabetic complications in combination with individual factors and to effectively predict multiple complications of diabetes. Methods. This was a real-world study. Data were collected from 40,913 participants with an average age of 48 years from the Department of Endocrinology of Ruijin Hospital in Shanghai. We proposed deep personal multitask prediction of diabetes complication with attentive interactions (DPMP-DC) to predict the five complication models of diabetes, including diabetic retinopathy, diabetic nephropathy, diabetic peripheral neuropathy, diabetic foot disease, and diabetic cardiovascular disease. Results. Our model has an accuracy rate of 88.01% for diabetic retinopathy, 89.58% for diabetic nephropathy, 85.77% for diabetic neuropathy, 80.56% for diabetic foot disease, and 82.48% for diabetic cardiovascular disease. The multitasking accuracy of multiple complications is 84.67%, and the missed diagnosis rate is 9.07%. Conclusion. We put forward the method of interactive integration with individual factors of patients for the first time in diabetic complications, which reflect the differences between individuals. Our multitask model using the hard sharing mechanism provides better prediction than prior single prediction models.
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Liu, Xiao-Chen, Xiao-Jie Chang, Si-Ren Zhao, Shan-Shan Zhu, Yan-Yan Tian, Jing Zhang, and Xin-Yue Li. "Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke." World Journal of Clinical Cases 12, no. 20 (July 16, 2024): 4048–56. http://dx.doi.org/10.12998/wjcc.v12.i20.4048.
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BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients. In addition to prolonging the hospitalization time and increasing the medical burden, post-stroke infection also significantly increases the risk of disease and death. Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke (AIS) is of great significance. It can guide clinical practice to perform corresponding prevention and control work early, minimizing the risk of stroke-related infections and ensuring favorable disease outcomes. AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model. METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected. Baseline data and post-stroke infection status of all study subjects were assessed, and the risk factors for post-stroke infection in patients with AIS were analyzed. RESULTS Totally, 48 patients with AIS developed stroke, with an infection rate of 23.3%. Age, diabetes, disturbance of consciousness, high National Institutes of Health Stroke Scale (NIHSS) score at admission, invasive operation, and chronic obstructive pulmonary disease (COPD) were risk factors for post-stroke infection in patients with AIS (P < 0.05). A nomogram prediction model was constructed with a C-index of 0.891, reflecting the good potential clinical efficacy of the nomogram prediction model. The calibration curve also showed good consistency between the actual observations and nomogram predictions. The area under the receiver operating characteristic curve was 0.891 (95% confidence interval: 0.839–0.942), showing predictive value for post-stroke infection. When the optimal cutoff value was selected, the sensitivity and specificity were 87.5% and 79.7%, respectively. CONCLUSION Age, diabetes, disturbance of consciousness, NIHSS score at admission, invasive surgery, and COPD are risk factors for post-stroke infection following AIS. The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.
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Chen, Xiao, Min Hou, and Dongxue Wang. "Machine learning-based model for prediction of deep vein thrombosis after gynecological laparoscopy: A retrospective cohort study." Medicine 103, no. 1 (January 5, 2024): e36717. http://dx.doi.org/10.1097/md.0000000000036717.
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Successful monitoring of deep vein thrombosis (DVT) remains a challenging problem after gynecological laparoscopy. Thus, this study aimed to create and validate predictive models for DVT with the help of machine learning (ML) algorithms. A total of 489 patients from the Cancer Biology Research Center, Tongji Hospital were included in the study between January 2017 and February 2023, and 35 clinical indicators from electronic health records (EHRs) were collected within 24h of patient admission. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO) regression. Then, the three commonly used DVT prediction models are random forest model (RFM), generalized linear regression model (GLRM), and artificial neural network model (ANNM). In addition, the predictive performance of various prediction models (i.e. the robustness and accuracy of predictions) is evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA), respectively. We found postoperative DVT in 41 (8.38%) patients. Based on the ML algorithm, a total of 13 types of clinical data were preliminarily screened as candidate variables for DVT prediction models. Among these, age, body mass index (BMI), operation time, intraoperative pneumoperitoneum pressure (IPP), diabetes, complication and D-Dimer independent risk factors for postoperative DVT and can be used as variables in ML prediction models. The RFM algorithm can achieve the optimal DVT prediction performance, with AUC values of 0.851 (95% CI: 0.793–0.909) and 0.862 (95% CI: 0.804–0.920) in the training and validation sets, respectively. The AUC values of the other two prediction models (ANNM and GLRM) range from 0.697 (95% CI: 0.639–0.755) and 0.813 (95% CI: 0.651–0.767). In summary, we explored the potential risk of DVT after gynecological laparoscopy, which helps clinicians identify high-risk patients before gynecological laparoscopy and make nursing interventions. However, external validation will be needed in the future.
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Healthcare Engineering, Journal of. "Retracted: Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning." Journal of Healthcare Engineering 2023 (September 20, 2023): 1. http://dx.doi.org/10.1155/2023/9891682.
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Dahab, Mahmoud, Ping Zhang, Samiah Hamad Al-Mijalli, and Emad M. Abdallah. "Unveiling the Anti-Cholera and Active Diabetic Renoprotective Compounds of Maqian Essential Oil: A Computational and Molecular Dynamics Study." Molecules 28, no. 24 (December 5, 2023): 7954. http://dx.doi.org/10.3390/molecules28247954.
Abstract:
Cholera is an exceptionally aggressive infectious disease characterized by the potential to induce acute, copious, watery diarrhea of considerable severity and renal inflammation. Diabetic nephropathy is a serious complication of diabetes mellitus that can lead to kidney failure through inflammation; thus, anti-inflammatory agents are promising therapies for diabetic nephropathy. Previous studies have shown that the essential oil of Zanthoxylum myriacanthum var. pubescens Huang, Maqian essential oil (MQEO), exhibits potent antibacterial, anti-inflammatory, and renoprotective activities in diabetic mice and has emerged as a potential therapeutic drug for the treatment of diabetic nephropathy complications. Therefore, the present study was carried out to screen the potential inhibition of cholera toxin and the diabetic renoprotective activity of MQEO through computational approaches. Twelve chemical constituents derived from MQEO were docked with cholera toxin and the target proteins involved in diabetic nephropathy, namely, TXNIP, Nrf2, and DPP IV, and, subsequently, the predictions of molecular dynamic simulations, the drug-likeness properties, and the ADMET properties were performed. α-terpineol showed high binding affinities toward the cholera toxin protein. For TXNIP, among all the chemical constituents, α-phellandrene and p-cymene showed strong binding affinities with the TXNIP protein and displayed relatively stable flexibility at the hinge regions of the protein, favorable physicochemical properties in the absence of hepatotoxicity, and low cytotoxicity. For Nrf2, α-terpineol exhibited the highest binding affinity and formed a very stable complex with Nrf2, which displayed high pharmacokinetic properties. All compounds had low free-binding energies when docked with the DPP IV protein, which suggests potent biological activity. In conclusion, based on a computational approach, our findings reveal that MQEO constituents have inhibitory activity against cholera toxin and are promising therapeutic agents for suppressing diabetic inflammation and for the treatment of diabetic nephropathy complications.
10
Alghamdi, Turki. "Prediction of Diabetes Complications Using Computational Intelligence Techniques." Applied Sciences 13, no. 5 (February 27, 2023): 3030. http://dx.doi.org/10.3390/app13053030.
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Diabetes is a complex disease that can lead to serious health complications if left unmanaged. Early detection and treatment of diabetes is crucial, and data analysis and predictive techniques can play a significant role. Data mining techniques, such as classification and prediction models, can be used to analyse various aspects of data related to diabetes, and extract useful information for early detection and prediction of the disease. XGBoost classifier is a machine learning algorithm that effectively predicts diabetes with high accuracy. This algorithm uses a gradient-boosting framework and can handle large and complex datasets with high-dimensional features. However, it is important to note that the choice of the best algorithm for predicting diabetes may depend on the specific characteristics of the data and the research question being addressed. In addition to predicting diabetes, data analysis and predictive techniques can also be used to identify risk factors for diabetes and its complications, monitor disease progression, and evaluate the effectiveness of treatments. These techniques can provide valuable insights into the underlying mechanisms of the disease and help healthcare providers make informed decisions about patient care. Data analysis and predictive techniques have the potential to significantly improve the early detection and management of diabetes, a fast-growing chronic disease that notable health hazards. The XGBoost classifier showed the most effectiveness, with an accuracy rate of 89%.
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Yagin, Fatma Hilal, Cemil Colak, Abdulmohsen Algarni, Yasin Gormez, Emek Guldogan, and Luca Paolo Ardigò. "Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy." Diagnostics 14, no. 13 (June 27, 2024): 1364. http://dx.doi.org/10.3390/diagnostics14131364.
Abstract:
Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). Methods: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro–Wilk test for normality, the Kruskal–Wallis H test for group differences, and the Mann–Whitney U test with Bonferroni correction for post-hoc comparisons. Results: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. Conclusion: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management.
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Veerabathiran, Ramakrishnan. "Macrosomia: A Serious Complication of Diabetes in Pregnancy." Diabetes & Obesity International Journal 8, no. 4 (2023): 1–6. http://dx.doi.org/10.23880/doij-16000280.
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Gestational diabetes mellitus (GDM) and its associated complication, macrosomia, pose significant challenges during pregnancy. This review explores the epidemiological aspects, pathophysiology, complications, and long-term consequences of these conditions, with a focus on the need for early detection and advanced management strategies. The prevalence of GDM varies globally, with particular attention to India, where the correlation between macrosomia and GDM is evident. Early prenatal monitoring and glycemic control have been identified as effective strategies to reduce macrosomia incidence. Understanding the pathophysiology of GDM reveals a complex interplay of β-cell dysfunction and chronic insulin resistance, resulting in elevated blood sugar levels. This disruption contributes to macrosomia, characterized by the birth of abnormally large infants. Complications associated with macrosomia are not limited to childbirth; mothers may experience protracted labor, uterine atony, and an increased risk of tears, while newborns face the challenge of shoulder dystocia and neonatal jaundice. Additionally, the enduring significance of exposure to GDM in utero is concerning, with evidence linking it to childhood obesity and metabolic syndrome. The review also discusses promising research directions. The study of glycation markers aims to improve macrosomia prediction, enabling better management and care for affected pregnancies. Moreover, the integration of mobile technology, such as the GDm health smartphone solution, offers remote monitoring of blood glucose levels and tailored feedback, potentially revolutionizing GDM management. In tackling the challenges presented by GDM and macrosomia, a multifaceted approach is essential. Early and effective prenatal care, coupled with vigilant glycemic control, is critical for the well-being of both mothers and their infants. Continued research into innovative screening and management methods will further enhance pregnancy outcomes and long-term health. Healthcare professionals, armed with a comprehensive understanding of these conditions and their extensive effects, play a pivotal role in supporting expectant mothers and ensuring a brighter and healthier future for both mother and child.
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Assegie, Tsehay Admassu, Tamilarasi Suresh, Raguraman Purushothaman, Sangeetha Ganesan, and Napa Komal Kumar. "Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier." Journal of Robotics and Control (JRC) 4, no. 4 (July 4, 2023): 452–57. http://dx.doi.org/10.18196/jrc.v4i4.18412.
Abstract:
Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned. With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.
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Bommala, Harikrishna, Kannedari Vamshi Krishna, Avusula Supriya, Rama Krishna Biradar, Bharath Mayabrahma, D. Ushasree, and Evgeny Vladimirovich Kotov. "Fine-Tunining the Future: Optimizing svm hyper-parameters or enhanced diabetes prediction." MATEC Web of Conferences 392 (2024): 01082. http://dx.doi.org/10.1051/matecconf/202439201082.
Abstract:
Millions of people throughout the globe suffer from diabetes mellitus, a debilitating illness that increases the risk of severe complications and early death. To take preventative measures and tailor treatment to each individual's needs, it is essential to identify diabetes early and estimate risk accurately. This research provides a data-driven strategy for predicting diabetes based on SVM models. This work uses a large dataset, including clinical and demographic data from a wide range of people, including those with and without diabetes, to conduct our analysis. A prediction model that divides people into diabetes and non-diabetic groups based on their input attributes is constructed using the SVM algorithm. Engineers use feature selection and other engineering methods to improve the model's efficacy and readability. The results of the research show that the SVM algorithm is capable of producing reliable predictions of diabetes risk. Measures of the model's efficacy include its sensitivity to false positives, specificity in identifying true positives, and area under the Receiver Operating Characteristics curve (AUC-ROC). In addition, feature significance analysis improves the model's interpretability by illuminating the most critical risk variables for diabetes. The accuracy and interpretability of the proposed SVM-based diabetic prediction model are promising, making it a valuable tool for healthcare practitioners and policymakers to identify those at high risk of developing diabetes and modify preventative measures and interventions appropriately.
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Jian, Yazan, Michel Pasquier, Assim Sagahyroon, and Fadi Aloul. "A Machine Learning Approach to Predicting Diabetes Complications." Healthcare 9, no. 12 (December 9, 2021): 1712. http://dx.doi.org/10.3390/healthcare9121712.
Abstract:
Diabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for building different models to predict and classify eight diabetes complications. The complications include metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy. For this study, a dataset collected by the Rashid Center for Diabetes and Research (RCDR) located in Ajman, UAE, was utilized. The dataset consists of 884 records with 79 features. Some essential preprocessing steps were applied to handle the missing values and unbalanced data problems. Furthermore, feature selection was performed to select the top five and ten features for each complication. The final number of records used to train and build the binary classifiers for each complication was as follows: 428—metabolic syndrome, 836—dyslipidemia, 223—neuropathy, 233—nephropathy, 240—diabetic foot, 586—hypertension, 498—obesity, 228—retinopathy. Repeated stratified k-fold cross-validation (with k = 10 and a total of 10 repetitions) was employed for a better estimation of the performance. Accuracy and F1-score were used to evaluate the models’ performance reaching a maximum of 97.8% and 97.7% for accuracy and F1-scores, respectively. Moreover, by comparing the performance achieved using different attributes’ sets, it was found that by using a selected number of features, we can still build adequate classifiers.
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Yousefi, Leila, and Allan Tucker. "Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction." Intelligent Data Analysis 26, no. 2 (March 14, 2022): 501–24. http://dx.doi.org/10.3233/ida-205570.
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Predicting complications associated with complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors. To analyse the complications associated with complex disease, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation. This work proposes appropriate Intelligent Data Analysis methods for building Dynamic Bayesian networks with latent variables, applied to small-sized clinical data (a case of Type 2 Diabetes complications). First, it adopts a Time Series Bootstrapping approach to re-sample the rare complication class with a replacement with respect to the dynamics of disease progression. Then, a combination of the Induction Causation algorithm and Link Strength metric (which is called IC*LS approach) is applied on the bootstrapped data for incrementally identifying latent variables. The most highlighted contribution of this paper gained insight into the disease progression by interpreting the latent states (with respect to the associated distributions of complications). An exploration of inference methods along with confidence interval assessed the influences of these latent variables. The obtained results demonstrated an improvement in the prediction performance.
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Brink, Huguette S., Aart Jan van der Lely, and Joke van der Linden. "The potential role of biomarkers in predicting gestational diabetes." Endocrine Connections 5, no. 5 (September 2016): R26—R34. http://dx.doi.org/10.1530/ec-16-0033.
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Gestational diabetes (GD) is a frequent complication during pregnancy and is associated with maternal and neonatal complications. It is suggested that a disturbing environment for the foetus, such as impaired glucose metabolism during intrauterine life, may result in enduring epigenetic changes leading to increased disease risk in adult life. Hence, early prediction of GD is vital. Current risk prediction models are based on maternal and clinical parameters, lacking a strong predictive value. Adipokines are mainly produced by adipocytes and suggested to be a link between obesity and its cardiovascular complications. Various adipokines, including adiponectin, leptin and TNF&, have shown to be dysregulated in GD. This review aims to outline biomarkers potentially associated with the pathophysiology of GD and discuss the role of integrating predictive biomarkers in current clinical risk prediction models, in order to enhance the identification of those at risk.
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V, Sathya. "Maternal Serum Biomarkers for the Early Prediction of Gestational Diabetes Mellitus." Diabetes & Obesity International Journal 4, no. 1 (2019): 1–7. http://dx.doi.org/10.23880/doij-16000190.
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The prevalence of gestational diabetes mellitus (GDM) is increasing because of the worldwide obesity/diabetes epidemic. The complications of untreated GDM affect both the mother and baby and include complications during pregnancy as well as increased risk of subsequent type-2 Diabetes in mother and offspring. Screening for glucose intolerance during pregnancy provides an opportunity to offer management to those women diagnosed with gestational diabetes mellitus (GDM). Standard tests for hyperglycemia in diabetes, such as fasting glucose and glycosylated hemoglobin (HbA1c) are not currently recommended for GDM screening. Instead, an oral glucose tolerance test (OGTT) is specified, which is invasive, time-consuming, and not easily accessible to many at-risk populations. In this article, we describe a multianalyte maternal serum profile test that incorporates novel glycoprotein biomarkers include endocrine and metabolic hormones like Myokines & cytokines (Adiponectin, Irisin, HCG, Insulin, Ferritin, PAPP-A, Resistin, CRP, Fibronectin, Leptin and SHBG). Further studies are warranted to determine the Reliability of these markers.
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Rachata, Napa, Punnarumol Temdee, Worasak Rueangsirarak, and Chayapol Kamyod. "Fuzzy based Risk Predictive Model for Cardiovascular Complication of Patient with Type 2 Diabetes Mellitus and Hypertension." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 1 (June 23, 2019): 49–58. http://dx.doi.org/10.37936/ecti-cit.2019131.132114.
Abstract:
Cardiovascular diseases are chronic diseases that cause serious morbidity and mortality worldwide. Unfortunately, the patients with type 2 diabetes mellitus and hypertension have a high risk of having a cardiovascular complication. For these reasons, patients with type 2 diabetes mellitus and hypertension should be aware of cardiovascular complication along their healthcare journey. To prevent cardiovascular complication from diabetes and hypertension, accurate risk prediction is required for a long term self-management process. Consequently, this paper proposes a fuzzy logic based method for predicting cardiovascular risk particularly for a patient with type 2 diabetes mellitus and hypertension. This paper also proposes a set of factors based on the patient’s lifestyle as the key factors besides clinical factors because of their implicit impact on the quality of life of the patient. The proposed model thus employs 15 predictors for both clinical and lifestyle risk factors. Additionally, the proposed model is constructed based on the scientific data and implicit knowledge of the experts. The experiment with 121 patients shows that the proposed prediction model provides 96.69% accuracy compared to those decided by the experts.
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Hayuningtyas, Ratih Yulia, and Retno Sari. "Implementasi Data Mining Dengan Algoritma Multiple Linear Regression Untuk Memprediksi Penyakit Diabetes." Jurnal Teknik Komputer 8, no. 1 (January 24, 2022): 40–44. http://dx.doi.org/10.31294/jtk.v8i1.11552.
Abstract:
According to WHO, diabetes is a metabolic disorder characterized by high levels of sugar in the blood. Diabetes is a deadly disease if the sufferer cannot control it and it will become a complication. Many people are affected by diabetes and find out too late, so that at the time of treatment the condition has complications. Early detection of diabetes is very helpful for sufferers to avoid complications that will occur. Therefore we need a data mining technique that can process data and prevent diabetes from an early age. Data mining is a process of extracting knowledge from a number of data to find a pattern. Data mining has been widely used, one of which is the prediction method to find out people with diabetes. There are so many prediction methods available, one of which is linear regression, where this method uses dependent and independent attributes. In this study, the multiple linear regression method is used to predict diabetes, and evaluates using RMSE (root mean square error). The results of this study produce an RMSE value of 0.403, the RMSE test uses cross validation by changing the number of validation value
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Rozhkova, O. V., O. V. Remneva, and N. V. Trukhacheva. "Prediction of perinatal complications of gestational diabetes." Fundamental and Clinical Medicine 4, no. 4 (December 28, 2019): 19–25. http://dx.doi.org/10.23946/2500-0764-2019-4-4-19-25.
Abstract:
Aim. To develop a tool for the prediction of perinatal complications in pregnant women with gestational diabetes utilizing conventional and ultrasound markers of diabetic fetopathy.Materials and Methods. We analyzed 128 case histories of women who suffered from gestational diabetes. Among their newborns, 35 had clinical manifestations of maternal hyperglycemia (birth weight > 90th percentile, neonatal hyperglycemia, respiratory distress syndrome, and neonatal jaundice) while 74 were free of the indicated signs and symptoms.Results. Risk factors of maternal hyperglycemia manifestations in neonates included family history of diabetes mellitus type 2, obesity, and pre-eclampsia. Maternal hyperglycemia was significantly associated with the higher risk of adverse perinatal outcomes. A combination of ≥ 4 ultrasound оценке markers of a diabetic fetopathy permitted the diagnosis of the fetal macrosomia. Conclusion. Ultrasound markers of diabetic fetopathy have limited sensitivity in the prediction of perinatal complications after gestational diabetes.
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Tetreault, Lindsay, Gamaliel Tan, Branko Kopjar, Pierre Côté, Paul Arnold, Natalia Nugaeva, Giuseppe Barbagallo, and Michael G. Fehlings. "Clinical and Surgical Predictors of Complications Following Surgery for the Treatment of Cervical Spondylotic Myelopathy." Neurosurgery 79, no. 1 (November 25, 2015): 33–44. http://dx.doi.org/10.1227/neu.0000000000001151.
Abstract:
Abstract BACKGROUND Surgery for cervical spondylotic myelopathy (CSM) is generally safe and effective. Nonetheless, complications occur in 11% to 38% of patients. Knowledge of important predictors of complications will help clinicians identify high-risk patients and institute prevention and management strategies. OBJECTIVE To identify clinical and surgical predictors of perioperative complications in CSM patients. METHODS Four hundred seventy-nine surgical CSM patients were enrolled in the prospective CSM-International study at 16 sites. A panel of physicians reviewed all adverse events and classified each as related or unrelated to surgery. Univariate analyses were performed to determine differences between patients who experienced a perioperative complication and those who did not. A complication prediction rule was developed using multiple logistic regression. RESULTS Seventy-eight patients experienced 89 perioperative complications (16.25%). On univariate analysis, the major clinical risk factors were ossification of the posterior longitudinal ligament (OPLL) (P = .055), number of comorbidities (P = .002), comorbidity score (P = .006), diabetes mellitus (P = .001), and coexisting gastrointestinal (P = .039) and cardiovascular (P = .046) disorders. Patients undergoing a 2-stage surgery (P = .002) and those with a longer operative duration (P = .001) were at greater risk of perioperative complications. A final prediction model consisted of diabetes mellitus (odds ratio [OR] = 1.96, P = .060), number of comorbidities (OR = 1.20, P = .069), operative duration (OR = 1.07, P = .002), and OPLL (OR = 1.75, P = .040). CONCLUSION Surgical CSM patients have a higher risk of perioperative complications if they have a greater number of comorbidities, coexisting diabetes mellitus, OPLL, and a longer operative duration. Surgeons can use this information to discuss the risks and benefits of surgery with patients, to plan case-specific preventive strategies, and to ensure appropriate management in the perioperative period.
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Kathiravan A., Dr T. Ananth kumar, and Dr P. Kanimozhi. "A Survey on Implementing Machine Learning Algorithm to Predict Diabetes Stages and Preventing Elevated Blood Glucose Levels." Irish Interdisciplinary Journal of Science & Research 07, no. 04 (2023): 18–24. http://dx.doi.org/10.46759/iijsr.2023.7403.
Abstract:
Diabetes, a chronic metabolic disorder, A health condition with rising prevalence. Accurate prediction of diabetes stages and proactive management of elevated blood glucose levels are crucial for effective treatment and prevention of complications. This study investigates the implementation of machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbors (KNN), to address these critical aspects of diabetes care. In this research, a comprehensive data-set comprising clinical and demographic information of individuals was utilized. Data preprocessing techniques, including feature selection, normalization, and handling of missing values, were employed to prepare the data-set for modeling. These Algorithms were trained and evaluated for their effectiveness in predicting diabetes stages and identifying individuals at risk of elevated blood glucose levels. The previous techniques used in diabetes prediction performs with moderate accuracy and optimization which will not sufficient to attain maximum level of prediction.
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Song, Xing, Lemuel Russ Waitman, Alan SL Yu, David C. Robbins, Yong Hu, and Mei Liu. "Longitudinal Risk Prediction of Chronic Kidney Disease in Diabetic Patients using Temporal-Enhanced Gradient Boosting Machine: Retrospective Cohort Study." JMIR Medical Informatics 8, no. 1 (January 24, 2020): e15510. http://dx.doi.org/10.2196/15510.
Abstract:
Background Artificial intelligence–enabled electronic health record (EHR) analysis can revolutionize medical practice from diagnosis and prediction of complex diseases to making recommendations in patient care, especially for chronic conditions such as chronic kidney disease (CKD), which is one of the most frequent complications in patients with diabetes and is associated with substantial morbidity and mortality. Objective Longitudinal prediction of health outcome requires effective representation of temporal data in EHR. In this study, we proposed a novel temporal-enhanced gradient boosting machine (GBM) model that dynamically updates and ensembles learners based on new events in patient timelines to improve the prediction accuracy of CKD among patients with diabetes. Methods Using a broad spectrum of deidentified EHR data on a retrospective cohort of 14,039 adult patients with type 2 diabetes and GBM as the base learner, we validated our proposed Landmark-Boosting model against three state-of-the-art temporal models for rolling predictions of 1-year CKD risk. Results The proposed model uniformly outperformed other models, achieving an area under receiver operating curve of 0.83 (95% CI 0.76-0.85), 0.78 (95% CI 0.75-0.82), and 0.82 (95% CI 0.78-0.86) in predicting CKD risk with automatic accumulation of new data in later years (years 2, 3, and 4 since diabetes mellitus onset, respectively). The Landmark-Boosting model also maintained the best calibration across moderate- and high-risk groups and over time. The experimental results demonstrated that the proposed temporal model can not only accurately predict 1-year CKD risk but also improve performance over time with additionally accumulated data, which is essential for clinical use to improve renal management of patients with diabetes. Conclusions Incorporation of temporal information in EHR data can significantly improve predictive model performance and will particularly benefit patients who follow-up with their physicians as recommended.
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Kumari, Gorli L. Aruna, Poosapati Padmaja, and Jaya G. Suma. "A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (April 1, 2022): 404. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp404-413.
Abstract:
Hyperglycemia arises due to diabetes mellitus, which is a persistent and life-threatening ailment. In this paper deep convolution neural network can be embedded to long short-term memory networks to recognize early prediction of diabetes and to decrease the complications that can be occurred through diabetes irrespective to the age. Diabetes problem is being gradually growing and presently, it is reported as a significant cause of death in the top spot. According to the recent studies 48% of overall world population will be affected by diabetes by 2045. If diabetes unidentified in early stages, it may cause other additional cardiac problems. In the proposed based work, a deep learning framework deep combination of convolution neural network and long short-term memory is proposed by embedding both to leverage their respective advantages for diabetes recognition and to allow early prediction of diabetes to avoid other complications. The experimental evolution on the bunch mark of diabetes data set demonstrates the proposed model embedded deep long short-term memory outperforms other machine learning and conventional deep learning approaches. The proposed algorithm in this paper outperforms existing techniques and evaluates total effectiveness and accuracy of predicting whether a person will suffer from diabetes.
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Kartina Diah Kusuma Wardani and Memen Akbar. "Diabetes Risk Prediction using Feature Importance Extreme Gradient Boosting (XGBoost)." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 4 (August 12, 2023): 824–31. http://dx.doi.org/10.29207/resti.v7i4.4651.
Abstract:
Diabetes results from impaired pancreas function as a producer of insulin and glucagon hormones, which regulate glucose levels in the blood. People with diabetes today are not only experienced adults, but pre-diabetes has been identified since the age of children and adolescents. Early prediction of diabetes can make it easier for doctors and patients to intervene as soon as possible so that the risk of complications can be reduced. One of the uses of medical data from diabetes patients is used to produce a model that can be used by medical staff to predict and identify diabetes in patients. Various techniques are used to provide the earliest possible prediction of diabetes based on the symptoms experienced by diabetic patients, including using machine learning. People can use Machine Learning to generate models based on historical data of diabetic patients, and predictions are made with the model. In this study, extreme gradient boosting is the machine learning technique to predict diabetes (xgboost) using Feature Importance XGBoost. The diabetes dataset used in this study comes from the Early stage diabetes risk prediction dataset published by UCI Machine Learning, which has 520 records and 16 attributes. The diabetes prediction model using xgboost is displayed as a tree. The model accuracy result in this study was 98.71%, for the F1 score was 98.18%. While the accuracy obtained based on the best 10 attributes using the XGBoost feature importance are 98.72%.
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Gardner, Clarissa, Deborah Wake, Doogie Brodie, Alex Silverstein, Sophie Young, Scott Cunningham, Chris Sainsbury, et al. "Evaluation of prototype risk prediction tools for clinicians and people living with type 2 diabetes in North West London using the think aloud method." DIGITAL HEALTH 9 (January 2023): 205520762211286. http://dx.doi.org/10.1177/20552076221128677.
Abstract:
The prevalence of type 2 diabetes in North West London (NWL) is relatively high compared to other parts of the United Kingdom with outcomes suboptimal. This presents a need for more effective strategies to identify people living with type 2 diabetes who need additional support. An emerging subset of web-based interventions for diabetes self-management and population management has used artificial intelligence and machine learning models to stratify the risk of complications from diabetes and identify patients in need of immediate support. In this study, two prototype risk prediction tools on the MyWay Diabetes and MyWay Clinical platforms were evaluated with six clinicians and six people living with type 2 diabetes in NWL using the think aloud method. The results of the sessions with people living with type 2 diabetes showed that the concept of the tool was intuitive, however, more instruction on how to correctly use the risk prediction tool would be valuable. The feedback from the sessions with clinicians was that the data presented in the tool aligned with the key diabetes targets in NWL, and that this would be useful for identifying and inviting patients to the practice who are overdue for tests and at risk of complications. The findings of the evaluation have been used to support the development of the prototype risk predictions tools. This study demonstrates the value of conducting usability testing on web-based interventions designed to support the targeted management of type 2 diabetes in local communities.
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Saxena, Roshi, Sanjay Kumar Sharma, Manali Gupta, and G. C. Sampada. "A Comprehensive Review of Various Diabetic Prediction Models: A Literature Survey." Journal of Healthcare Engineering 2022 (April 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/8100697.
Abstract:
Diabetes is a chronic disease characterized by a high amount of glucose in the blood and can cause too many complications also in the body, such as internal organ failure, retinopathy, and neuropathy. According to the predictions made by WHO, the figure may reach approximately 642 million by 2040, which means one in a ten may suffer from diabetes due to unhealthy lifestyle and lack of exercise. Many authors in the past have researched extensively on diabetes prediction through machine learning algorithms. The idea that had motivated us to present a review of various diabetic prediction models is to address the diabetic prediction problem by identifying, critically evaluating, and integrating the findings of all relevant, high-quality individual studies. In this paper, we have analysed the work done by various authors for diabetes prediction methods. Our analysis on diabetic prediction models was to find out the methods so as to select the best quality researches and to synthesize the different researches. Analysis of diabetes data disease is quite challenging because most of the data in the medical field are nonlinear, nonnormal, correlation structured, and complex in nature. Machine learning-based algorithms have been ruled out in the field of healthcare and medical imaging. Diabetes mellitus prediction at an early stage requires a different approach from other approaches. Machine learning-based system risk stratification can be used to categorize the patients into diabetic and controls. We strongly recommend our study because it comprises articles from various sources that will help other researchers on various diabetic prediction models.
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El-Sofany, Hosam, Samir A. El-Seoud, Omar H. Karam, Yasser M. Abd El-Latif, and Islam A. T. F. Taj-Eddin. "A Proposed Technique Using Machine Learning for the Prediction of Diabetes Disease through a Mobile App." International Journal of Intelligent Systems 2024 (January 9, 2024): 1–13. http://dx.doi.org/10.1155/2024/6688934.
Abstract:
With the increasing prevalence of diabetes in Saudi Arabia, there is a critical need for early detection and prediction of the disease to prevent long-term health complications. This study addresses this need by using machine learning (ML) techniques applied to the Pima Indians dataset and private diabetes datasets through the implementation of a computerized system for predicting diabetes. In contrast to prior research, this study employs a semisupervised model combined with strong gradient boosting, effectively predicting diabetes-related features of the dataset. Additionally, the researchers employ the SMOTE technique to deal with the problem of imbalanced classes. Ten ML classification techniques, including logistic regression, random forest, KNN, decision tree, bagging, AdaBoost, XGBoost, voting, SVM, and Naive Bayes, are evaluated to determine the algorithm that produces the most accurate diabetes prediction. The proposed approach has achieved impressive performance. For the private dataset, the XGBoost algorithm with SMOTE achieved an accuracy of 97.4%, an F1 coefficient of 0.95, and an AUC of 0.87. For the combined datasets, it achieved an accuracy of 83.1%, an F1 coefficient of 0.76, and an AUC of 0.85. To understand how the model predicts the final results, an explainable AI technique using SHAP methods is implemented. Furthermore, the study demonstrates the adaptability of the proposed system by applying a domain adaptation method. To further enhance accessibility, a mobile app has been developed for instant diabetes prediction based on user-entered features. This study contributes novel insights and techniques to the field of ML-based diabetic prediction, potentially aiding in the early detection and management of diabetes in Saudi Arabia.
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Almheiri, Ali, Amna Alhammadi, Fatima AlShehhi, Asma Mohammad, Rodha Alshamsi, Khaled Alzaman, Saima Jabeen, and Burhan Haq. "Biomarkers for Prediabetes, Type 2 Diabetes, and Associated Complications." American Journal of Health, Medicine and Nursing Practice 9, no. 2 (September 27, 2023): 1–21. http://dx.doi.org/10.47672/ajhmn.1592.
Abstract:
Purpose: Diabetes mellitus is a chronic disorder caused by high blood glucose levels due to insulin resistance or insufficient insulin production in pancreatic β-cells. Due to its fastest-growing public health concerns worldwide, it is important to evaluate metabolic profile abnormalities before pre-diabetes or T2DM to anticipate and prevent disease progression. The purpose of the study was to examine the metabolite biomarkers by systematic review and meta-analysis to support early detection of pre-diabetes and T2DM.
Methodology: Studies published from the earliest online through May 31, 2023, were searched in the Cochrane Library, EMBASE, PubMed, and Scopus. Article titles, abstracts, and complete texts were reviewed after duplicate records were eliminated. Two writers (Long and Yang) created the following inclusion criteria for the publications before literature screening: The study was conducted on humans, did not involve gestational diabetes mellitus (GDM), type 1 diabetes mellitus (T1DM), or subjects under 18 years old, included a diabetic or prediabetes group, and followed international diagnostic guidelines (American Diabetes Association, 2013).
Findings: The study aimed to review the biomarkers that have been utilized for diabetes in previous research. The comparison of the biomarkers mentioned in the provided information revealed a complex interplay of factors influencing the risk and management of Type 2 Diabetes (T2D). These biomarkers encompass genetic, lifestyle, environmental, and insulin-related factors, each with varying degrees of accuracy and specificity in predicting T2D risk or guiding its management.
Recommendations: The research will help in spreading awareness among people regarding the identification of diabetes as understanding biomarker-based screening's economic impact can inform healthcare policies. Future studies should validate these biomarkers' diagnostic capacities across varied populations and circumstances. Assessment of these biomarkers' predictive usefulness should be done over time via longitudinal research. Understanding biomarker alterations and diabetes progression improves risk prediction.
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Rasha Rokan Ismail. "Early diagnosing diabetes using data mining algorithms." Global Journal of Engineering and Technology Advances 16, no. 2 (August 30, 2023): 106–13. http://dx.doi.org/10.30574/gjeta.2023.16.2.0141.
Abstract:
Diabetes has become a widespread and long lasting condition that continues to impact an increasing number of individuals across the globe. It is crucial to highlight the significance of accurately identifying, predicting, managing and treating diabetes in order to address this growing concern. Utilizing sophisticated data analysis techniques to examine data relating to diabetes can significantly enhance the early detection and prediction of this ailment, along with its associated complications like low or high blood sugar levels. The findings clearly demonstrate that the decision tree algorithm proves to be the most effective approach in promptly diagnosing diabetes patients and ensuring they receive timely access to suitable treatment options.
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Ginting, Rapael Ginting, Ermi Girsang, Johannes Bastira Ginting, and Hartono Hartono. "ANALISIS DETERMINAN DAN PREDIKSI PENYAKIT DIABETES MELITUS TIPE 2 MENGGUNAKAN METODE MACHINE LEARNING: SCOPING REVIEW." Jurnal Maternitas Kebidanan 7, no. 1 (April 16, 2022): 58–72. http://dx.doi.org/10.34012/jumkep.v7i1.2538.
Abstract:
ABSTRACT The prevalence of diabetes mellitus is increasing globally, nationally, and regionally, and most of them are type 2 diabetes mellitus, which can cause complications, economic losses, and death. The purpose of this study is to examine the analysis of determinants and predictions of type 2 diabetes mellitus using machine learning methods. This study uses the scoping review method to view, accumulate and synthesize the results of previous studies on the analysis of determinants and predictions of type 2 diabetes mellitus using machine learning methods. The inclusion criteria in this study were articles published in the indexed journal database PubMed, Google Scholar, Crossref in English and Indonesian, journals published in the 2017-2021 range and 15 articles that met the inclusion criteria. The search results were a total of 860 articles from 3 databases (PubMed, Google Scholar, Crossref) in which 98 of them were duplicate articles and were excluded. Of the remaining 762 articles, 142 were not full text and 605 were excluded after eligibility screening because they were irrelevant. The remaining 15 articles were systematically reviewed and qualitatively analyzed using the NVIVO-12 Plus application. From the analysis of previous studies concluded that age, obesity, family history of disease, and lack of physical activity are risk factors for type 2 diabetes mellitus, while the gender variable from the analysis of previous research shows that there is no significant relationship between gender and type 2 diabetes. With early prediction of type 2 diabetes mellitus preventive measures, treatment can be carried out immediately and reduce the incidence of complications that can worsen the condition of people with type 2 diabetes.
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Ziajor, Seweryn, Justyna Tomasik, Piotr Sajdak, Mikołaj Turski, Artur Bednarski, Marcel Stodolak, Łukasz Szydłowski, et al. "The use of artificial intelligence in the diagnosis and detection of complications of diabetes." Journal of Education, Health and Sport 65 (April 11, 2024): 11–27. http://dx.doi.org/10.12775/jehs.2024.65.001.
Abstract:
Introduction: Diabetes poses a significant global health challenge, impacting patient well-being and longevity. Despite advances in diagnosis and treatment, the prevalence of diabetes continues to rise, with projections indicating a substantial increase in affected individuals in the coming years. The complications of diabetes, including cardiovascular disease, retinopathy, nephropathy, and neuropathy, underscore the importance of early detection and management. In this context, artificial intelligence (AI) offers promising opportunities to revolutionize diabetes care, enabling faster diagnostics, more effective treatment strategies.
Description of the State of Knowledge: Artificial intelligence (AI) has emerged as a transformative force in healthcare, leveraging machine learning and deep learning algorithms to analyze vast amounts of medical data. These algorithms enable more accurate diagnosis, prediction of disease onset, and early detection of complications associated with diabetes. Machine learning models, including support vector machines and neural networks, have shown promise in identifying diabetes risk factors and predicting disease progression. Deep learning techniques, with their ability to analyze complex data patterns, offer further insights into diabetes diagnosis. Additionally, fuzzy cognitive maps provide a framework for decision-making based on patient data, enhancing early detection efforts.
Summary: Artificial intelligence holds immense potential to transform diabetes care, offering solutions for early detection, personalized treatment, and improved patient outcomes. By harnessing the power of AI algorithms, healthcare providers can enhance diagnostic accuracy, predict disease progression, and implement targeted interventions.
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Agliata, Antonio, Deborah Giordano, Francesco Bardozzo, Salvatore Bottiglieri, Angelo Facchiano, and Roberto Tagliaferri. "Machine Learning as a Support for the Diagnosis of Type 2 Diabetes." International Journal of Molecular Sciences 24, no. 7 (April 5, 2023): 6775. http://dx.doi.org/10.3390/ijms24076775.
Abstract:
Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual’s health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual’s risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics’ (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.
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Nijpels, Giel, Joline WJ Beulens, Amber AWA van der Heijden, and Petra J. Elders. "Innovations in personalised diabetes care and risk management." European Journal of Preventive Cardiology 26, no. 2_suppl (November 26, 2019): 125–32. http://dx.doi.org/10.1177/2047487319880043.
Abstract:
Type 2 diabetes is associated with an increased risk of developing macro and microvascular complications. Nevertheless, there is substantial heterogeneity between people with type 2 diabetes in their risk of developing such complications. Personalised medicine for people with type 2 diabetes may aid in efficient and tailored diabetes care for those at increased risk of developing such complications. Recently, progress has been made in the development of personalised diabetes care in several areas. Particularly for the risk prediction of cardiovascular disease, retinopathy and nephropathy, innovative methods have been developed for prediction and tailored monitoring or treatment to prevent such complications. For other complications or subpopulations of people with type 2 diabetes, such as the frail elderly, efforts are currently ongoing to develop such methods. In this review, we discuss the recent developments in innovations of personalised diabetes care for different complications and subpopulations of people with type 2 diabetes, their performance and modes of application in clinical practice.
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Tanabe, Hayato, Haruka Saito, Akihiro Kudo, Noritaka Machii, Hiroyuki Hirai, Gulinu Maimaituxun, Kenichi Tanaka, et al. "Factors Associated with Risk of Diabetic Complications in Novel Cluster-Based Diabetes Subgroups: A Japanese Retrospective Cohort Study." Journal of Clinical Medicine 9, no. 7 (July 2, 2020): 2083. http://dx.doi.org/10.3390/jcm9072083.
Abstract:
Diabetes is a complex and heterogeneous disease, making the prediction of the risks of diabetic complications challenging. Novel adult-onset diabetes subgroups have been studied using cluster analysis, but its application in East Asians remains unclear. We conducted a retrospective cohort study to elucidate the clinical utility of cluster-based subgroup analysis in the Japanese population. Cluster analysis based on anti-glutamate decarboxylase antibody (GAD antibody) levels, age at diagnosis, body mass index (BMI), hemoglobin A1c (A1c), and homeostatic model assessment 2 estimates of β-cell function and insulin resistance was performed in 1520 diabetic patients. The risk of developing diabetic complications was analyzed using Kaplan–Meier analysis and the Cox proportional hazards model. By cluster analysis, we identified five distinct subgroups of adult-onset diabetes in the Japanese population. The risk of diabetic complications varied greatly among the clusters. Patients with severe autoimmune diabetes or severe insulin deficiency diabetes were at an increased risk of diabetic retinopathy, and those with severe insulin resistant diabetes (SIRD) had the highest risk of developing diabetic kidney disease (DKD). After adjusting for uncorrectable and correctable risk factors, SIRD was found to be an independent risk factor for DKD. In conclusion, we identified five subgroups of adult-onset diabetes and the risk factors for diabetic complications in the Japanese population. This new classification system can be effective in predicting the risk of diabetic complications and for providing optimal treatment.
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Lu, Huiqi Y., Ping Lu, Jane E. Hirst, Lucy Mackillop, and David A. Clifton. "A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus." Sensors 23, no. 18 (September 20, 2023): 7990. http://dx.doi.org/10.3390/s23187990.
Abstract:
Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. The objectives of this study are to (1) develop benchmark glucose prediction models with long short-term memory (LSTM) recurrent neural network models using time-series data collected from the GDm-Health platform, (2) compare the prediction accuracy with published results, and (3) suggest an optimized clinical review schedule with the potential to reduce the overall number of blood tests for mothers with stable and within-range glucose measurements. A total of 190,396 BG readings from 1110 patients were used for model development, validation and testing under three different prediction schemes: 7 days of BG readings to predict the next 7 or 14 days and 14 days to predict 14 days. Our results show that the optimized BG schedule based on a 7-day observational window to predict the BG of the next 14 days achieved the accuracies of the root mean square error (RMSE) = 0.958 ± 0.007, 0.876 ± 0.003, 0.898 ± 0.003, 0.622 ± 0.003, 0.814 ± 0.009 and 0.845 ± 0.005 for the after-breakfast, after-lunch, after-dinner, before-breakfast, before-lunch and before-dinner predictions, respectively. This is the first machine learning study that suggested an optimized blood glucose monitoring frequency, which is 7 days to monitor the next 14 days based on the accuracy of blood glucose prediction. Moreover, the accuracy of our proposed model based on the fingerstick blood glucose test is on par with the prediction accuracies compared with the benchmark performance of one-hour prediction models using continuous glucose monitoring (CGM) readings. In conclusion, the stacked LSTM model is a promising approach for capturing the patterns in time-series data, resulting in accurate predictions of BG levels. Using a deep learning model with routine fingerstick glucose collection is a promising, predictable and low-cost solution for BG monitoring for women with gestational diabetes.
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Luo, Xin, Jijia Sun, Hong Pan, Dian Zhou, Ping Huang, Jingjing Tang, Rong Shi, Hong Ye, Ying Zhao, and An Zhang. "Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining." PLOS ONE 18, no. 8 (August 8, 2023): e0289749. http://dx.doi.org/10.1371/journal.pone.0289749.
Abstract:
In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell’s concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744−0.792) and 0.745 (95% CI, 0.669−0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30–54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.
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Vijayan, Midhula, and Venkatakrishnan S. "A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet." Diagnostics 13, no. 4 (February 17, 2023): 774. http://dx.doi.org/10.3390/diagnostics13040774.
Abstract:
The aim of this study is to develop a computer-assisted solution for the efficient and effective detection of diabetic retinopathy (DR), a complication of diabetes that can damage the retina and cause vision loss if not treated in a timely manner. Manually diagnosing DR through color fundus images requires a skilled clinician to spot lesions, but this can be challenging, especially in areas with a shortage of trained experts. As a result, there is a push to create computer-aided diagnosis systems for DR to help reduce the time it takes to diagnose the condition. The detection of diabetic retinopathy through automation is challenging, but convolutional neural networks (CNNs) play a vital role in achieving success. CNNs have been proven to be more effective in image classification than methods based on handcrafted features. This study proposes a CNN-based approach for the automated detection of DR using Efficientnet-B0 as the backbone network. The authors of this study take a unique approach by viewing the detection of diabetic retinopathy as a regression problem rather than a traditional multi-class classification problem. This is because the severity of DR is often rated on a continuous scale, such as the international clinical diabetic retinopathy (ICDR) scale. This continuous representation provides a more nuanced understanding of the condition, making regression a more suitable approach for DR detection compared to multi-class classification. This approach has several benefits. Firstly, it allows for more fine-grained predictions as the model can assign a value that falls between the traditional discrete labels. Secondly, it allows for better generalization. The model was tested on the APTOS and DDR datasets. The proposed model demonstrated improved efficiency and accuracy in detecting DR compared to traditional methods. This method has the potential to enhance the efficiency and accuracy of DR diagnosis, making it a valuable tool for healthcare professionals. The model has the potential to aid in the rapid and accurate diagnosis of DR, leading to the improved early detection, and management, of the disease.
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Lin, Ming-Yen, Jia-Sin Liu, Tzu-Yang Huang, Ping-Hsun Wu, Yi-Wen Chiu, Yihuang Kang, Chih-Cheng Hsu, Shang-Jyh Hwang, and Hsing Luh. "Data Analysis of the Risks of Type 2 Diabetes Mellitus Complications before Death Using a Data-Driven Modelling Approach: Methodologies and Challenges in Prolonged Diseases." Information 12, no. 8 (August 12, 2021): 326. http://dx.doi.org/10.3390/info12080326.
Abstract:
(1) Background: A disease prediction model derived from real-world data is an important tool for managing type 2 diabetes mellitus (T2D). However, an appropriate prediction model for the Asian T2D population has not yet been developed. Hence, this study described construction details of the T2D Holistic Care model via estimating the probability of diabetes-related complications and the time-to-occurrence from a population-based database. (2) Methods: The model was based on the database of a Taiwan pay-for-performance reimbursement scheme for T2D between November 2002 and July 2017. A nonhomogeneous Markov model was applied to simulate multistate (7 main complications and death) transition probability after considering the sequential and repeated difficulties. (3) Results: The Markov model was constructed based on clinical care information from 163,452 patients with T2D, with a mean follow-up time of 5.5 years. After simulating a cohort of 100,000 hypothetical patients over a 10-year time horizon based on selected patient characteristics at baseline, a good predicted complication and mortality rates with a small range of absolute error (0.3–3.2%) were validated in the original cohort. Better and optimal predictabilities were further confirmed compared to the UKPDS Outcomes model and applied the model to other Asian populations, respectively. (4) Contribution: The study provides well-elucidated evidence to apply real-world data to the estimation of the occurrence and time point of major diabetes-related complications over a patient’s lifetime. Further applications in health decision science are encouraged.
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CEVHER AKDULUM, Munire Funda, Erhan DEMİRDAĞ, Safarova SAHİLA, Mehmet ERDEM, and Ahmet ERDEM. "İlk Trimesterde Sistemik İmmün-İnflamasyon İndeksini Kullanarak Gestasyonel Diabetes Mellitus'u Tahmin Etme." Journal of Contemporary Medicine 12, no. 5 (September 30, 2022): 617–20. http://dx.doi.org/10.16899/jcm.1148179.
Abstract:
Aims: Gestational diabetes mellitus (GDM) is an inflammatory disorder. GDM raises the risk of pregnancy complications. Early recognition of GDM is critical to prevent complications. Systemic Immune-Inflammation Index (SII) is an index that shows the inflammatory response, we hypothesized that it might be associated to GDM. The purpose of this study was to determine the relationship between GDM and SII, as well as whether SII in the first trimester can predict GDM.
Material and Method: This retrospective cohort study was conducted between January 2021 and January 2022. 100 pregnant women were included in the study. The study group included 50 pregnant women who had been diagnosed with GDM. The control group consisted of the remaining 50 pregnant women who had not been diagnosed with GDM. SII values were calculated from the hemogram values of the patients at the first visit in the first trimester.
Results: There was a statistically significant difference between GDM and control groups in terms of SII, platelet, neutrophil, fT3, apgar 1 min and apgar 5 min measurements (p
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Abdalrada, Ahmad Shaker, Jemal Abawajy, Tahsien Al-Quraishi, and Sheikh Mohammed Shariful Islam. "Prediction of cardiac autonomic neuropathy using a machine learning model in patients with diabetes." Therapeutic Advances in Endocrinology and Metabolism 13 (January 2022): 204201882210866. http://dx.doi.org/10.1177/20420188221086693.
Abstract:
Background: Cardiac autonomic neuropathy (CAN) is a diabetes-related complication with increasing prevalence and remains challenging to detect in clinical settings. Machine learning (ML) approaches have the potential to predict CAN using clinical data. In this study, we aimed to develop and evaluate the performance of an ML model to predict early CAN occurrence in patients with diabetes. Methods: We used the diabetes complications screening research initiative data set containing 200 CAN-related tests on more than 2000 participants with type 2 diabetes in Australia. Data were collected on peripheral nerve functions, Ewing’s tests, blood biochemistry, demographics, and medical history. The ML model was validated using 10-fold cross-validation, of which 90% were used in training the model and the remaining 10% was used in evaluating the performance of the model. Predictive accuracy was assessed by area under the receiver operating curve, and sensitivity, specificity, positive predictive value, and negative predictive value. Results: Of the 237 patients included, 105 were diagnosed with an early stage of CAN while the remaining 132 were healthy. The ML model showed outstanding performance for CAN prediction with receiver operating characteristic curve of 0.962 [95% confidence interval (CI) = 0.939–0.984], 87.34% accuracy, and 87.12% sensitivity. There was a significant and positive association between the ML model and CAN occurrence ( p < 0.001). Conclusion: Our ML model has the potential to detect CAN at an early stage using Ewing’s tests. This model might be useful for healthcare providers for predicting the occurrence of CAN in patients with diabetes, monitoring the progression, and providing timely intervention.
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Febriani, Irene. "Undiagnosed Diabetes Prediction With Development of Scoring System Based on Risk Factors." Preventif : Jurnal Kesehatan Masyarakat 11, no. 1 (August 3, 2020): 9–21. http://dx.doi.org/10.22487/preventif.v11i1.54.
Abstract:
Undiagnosed Diabetes Mellitus (UDDM) is a person condition where has never been diagnosed with diabetes, but when a blood sugar examination survey shows the criteria for diabetes. Late diagnosis is a major problem for diabetes. In some cases, 50% of patients do not know the condition of diabetes mellitus, so the complications of diabetes mellitus become very severe. This study aimed to analyze dominant risk factors and make a risk score for Undiagnosed Diabetes Mellitus (UDDM). Making a risk score was carried out in 2016 based on secondary data from 2013 Basic Health Research (Riskesdas). The study population was adults aged ≥ 18 years, diagnosed early in diabetes during the 2013 Riskesdas, did not suffer from other chronic / contagious diseases. The sample size analyzed amounted to 18,963 people. The value of β coefficient from the results of multiple logistic regression predictive models was used to develop the score. The accuracy of the diabetes predictive score was assessed by ROC (Receiver Operating Characteristic). 2 prediction models developed into risk scores. Model 1 predictions of UDDM with 8 predictors (AUC 73.13%, sensitivity 29.19%, specificity 90.33%, PPV 25.32%, NPV 91.90%, cutoff ≥30), model 2 predictions of UDDM with 5 predictors (AUC 74.22%, sensitivity of 64.91%, spessivity 67.95%, PPV 18.37%, NPV 94.60%, cutoff 21). Undiagnosed diabetes risk factors and predictors in making scores on model 1 were gender, age, hypertension, body mass index, central obesity, HDL and LDL. In model 2 were gender, age, hypertension, body mass index, central obesity.
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Jose, Rejath, Faiz Syed, Anvin Thomas, and Milan Toma. "Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions." Applied Sciences 14, no. 5 (March 4, 2024): 2132. http://dx.doi.org/10.3390/app14052132.
Abstract:
The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based machine learning toolkit—to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics.
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Han, Yiteng, Qixuan Li, Jinghui Lou, and Jingrui Zhang. "Prediction of diabetes progress based on machine learning approach." Applied and Computational Engineering 37, no. 1 (January 22, 2024): 45–51. http://dx.doi.org/10.54254/2755-2721/37/20230468.
Abstract:
Uropathy is a serious chronic disease whose prevalence is increasing at an alarming rate. Early detection and prediction of diabetes in women is important because of the increased risk of diabetes-related complications during pregnancy. This study introduces machine learning models to assess the likelihood of diabetes in women. The importance of studying characteristics and improving prediction accuracy to understand the nuances of categorization. Specifically, for data preprocessing, experiments are conducted to solve the problem of missing values and outliers by replacing the zero values of certain features with the median values of the corresponding features. This step reduces the impact of less reliable data on model performance. As recognition models, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), and Random Forest (RF) are built. Performance analysis is performed along with a careful exploration of the hyperparameter space. Scores for Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) are used to compare various models. Different features affect the classification to different degrees. The experimental findings indicate that the modified random forest model demonstrates superior prediction accuracy and robustness. These findings can assist physicians in predicting a patient's risk of developing diabetes earlier.
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Zhan, Wenqiang, Jing Zhu, Xiaolin Hua, Jiangfeng Ye, Qian Chen, and Jun Zhang. "Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study." BMJ Open 11, no. 11 (November 2021): e054540. http://dx.doi.org/10.1136/bmjopen-2021-054540.
Abstract:
ObjectivesTo describe the epidemiology of uterine rupture in China from 2015 to 2016 and to build a prediction model for uterine rupture in women with a scarred uterus.SettingA multicentre cross-sectional survey conducted in 96 hospitals across China in 2015–2016.ParticipantsOur survey initially included 77 789 birth records from hospitals with 1000 or more deliveries per year. We excluded 2567 births less than 24 gestational weeks or unknown and 1042 births with unknown status of uterine rupture, leaving 74 180 births for the final analysis.Primary and secondary outcome measuresComplete and incomplete uterine rupture and the risk factors, and a prediction model for uterine rupture in women with scarred uterus (assigned each birth a weight based on the sampling frame).ResultsThe weighted incidence of uterine rupture was 0.18% (95% CI 0.05% to 0.23%) in our study population during 2015 and 2016. The weighted incidence of uterine rupture in women with scarred and intact uterus was 0.79% (95% CI 0.63% to 0.91%) and 0.05% (95% CI 0.02% to 0.13%), respectively. Younger or older maternal age, prepregnancy diabetes, overweight or obesity, complications during pregnancy (hypertensive disorders in pregnancy and gestational diabetes), low education, repeat caesarean section (≥2), multiple abortions (≥2), assisted reproductive technology, placenta previa, induce labour, fetal malpresentation, multiple pregnancy, anaemia, high parity and antepartum stillbirth were associated with an increased risk of uterine rupture. The prediction model including eight variables (OR >1.5) yielded an area under the curve (AUC) of 0.812 (95% CI 0.793 to 0.836) in predicting uterine rupture in women with scarred uterus with sensitivity and specificity of 77.2% and 69.8%, respectively.ConclusionsThe incidence of uterine rupture was 0.18% in this population in 2015–2016. The predictive model based on eight easily available variables had a moderate predictive value in predicting uterine rupture in women with scarred uterus. Strategies based on predictions may be considered to further reduce the burden of uterine rupture in China.
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Wan, Eric Yuk Fai, Esther Yee Tak Yu, Weng Yee Chin, Colman Siu Cheung Fung, Ruby Lai Ping Kwok, David Vai Kiong Chao, King Hong Chan, et al. "Ten-year risk prediction models of complications and mortality of Chinese patients with diabetes mellitus in primary care in Hong Kong: a study protocol." BMJ Open 8, no. 10 (October 2018): e023070. http://dx.doi.org/10.1136/bmjopen-2018-023070.
Abstract:
IntroductionDiabetes mellitus (DM) is a major disease burden worldwide because it is associated with disabling and lethal complications. DM complication risk assessment and stratification is key to cost-effective management and tertiary prevention for patients with diabetes in primary care. Existing risk prediction functions were found to be inaccurate in Chinese patients with diabetes in primary care. This study aims to develop 10-year risk prediction models for total cardiovascular diseases (CVD) and all-cause mortality among Chinese patients with DM in primary care.Methods and analysisA 10-year cohort study on a population-based primary care cohort of Chinese patients with diabetes, who were receiving care in the Hospital Authority General Outpatient Clinic on or before 1 January 2008, were identified from the clinical management system database of the Hospital Authority. All patients with complete baseline risk factors will be included and followed from 1 January 2008 to 31 December 2017 for the development and validation of prediction models. The analyses will be carried out separately for men and women. Two-thirds of subjects will be randomly selected as the training sample for model development. Cox regressions will be used to develop 10-year risk prediction models of total CVD and all-cause mortality. The validity of models will be tested on the remaining one-third of subjects by Harrell’s C-statistics and calibration plot. Risk prediction models for diabetic complications specific to Chinese patients in primary care will enable accurate risk stratification, prioritisation of resources and more cost-effective interventions for patients with DM in primary care.Ethics and disseminationThe study was approved by the Institutional Review Board of the University of Hong Kong—the Hospital Authority Hong Kong West Cluster (reference number: UW 15–258).Trial registration numberNCT03299010; Pre-results.
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Ndjaboue, Ruth, Gérard Ngueta, Charlotte Rochefort-Brihay, Daniel Guay, Sasha Delorme, Noah Ivers, Baiju Shah, et al. "Risk Prediction Models of Diabetes Complications: A Scoping Review." Canadian Journal of Diabetes 45, no. 7 (November 2021): S32. http://dx.doi.org/10.1016/j.jcjd.2021.09.095.
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Qian, Dongni, and Hong Gao. "Efficacy Analysis of Team-Based Nursing Compliance in Young and Middle-Aged Diabetes Mellitus Patients Based on Random Forest Algorithm and Logistic Regression." Computational and Mathematical Methods in Medicine 2022 (July 29, 2022): 1–7. http://dx.doi.org/10.1155/2022/3882425.
Abstract:
Objective. Long-term hyperglycemia in young and middle-aged diabetic patients can be complicated with diabetic ketoacidosis, stroke, myocardial infarction, infection, and other complications. The objective was to explore the application value of machine learning in predicting the recurrence risk of young and middle-aged diabetes patients with team-based nursing intervention. Methods. Clinical data of 80 patients with diabetes treated in the Department of Endocrinology from 2019 to 2020 were retrospectively collected. The data set was divided into 70% training set ( n =56) and 30% test set ( n =24). All the selected research cases were intervened by the team-based management mode involving family and clinical doctors and nurses. The degree of diabetes knowledge learning, the level of blood glucose changes, and the psychological state of the patients were evaluated. The random forest (RF) algorithm and logistic regression prediction model were constructed to predict the risk factors of diabetes recurrence. Results. There was no significant difference in the degree of diabetes knowledge learning, the level of blood glucose changes, and the psychological state between the training set and the test set ( P > 0.05 ). The FPG, HbA1c, and 2hPG of recurrence group patients were significantly higher than those of nonrecurrence group patients, and the difference was statistically significant ( P < 0.05 ). In descending order of importance based on the RF algorithm prediction model were glucose, BMI, age, insulin, pedigree function, skin thickness, and blood diastolic pressure. The accuracy of RF and logistic regression prediction models is 81.46% and 80.21%, respectively. Conclusion. The team-based nursing model has a good effect on the blood glucose control level of middle-aged and young diabetic patients. Age, BMI, and glucose values are risk factors for diabetes. The SF algorithm has a good effect on predicting the risk of diabetes, which is worthy of further clinical application.
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HD, Sowmya, Shreyaskar sanskar, Pawan tiwari, and Kishan kumar. "Diabetes Prediction Using Machine Learning Algorithm." International Journal of Innovative Research in Information Security 09, no. 03 (June 23, 2023): 115–20. http://dx.doi.org/10.26562/ijiris.2023.v0903.14.
Abstract:
Diabetes is a prevalent chronic illness affecting a vast population worldwide. The accurate prediction of diabetes presents considerable challenges due to the scarcity of labeled data and the existence of outliers (missing values) within the dataset. Early detection and effective management of diabetes are crucial in preventing severe complications that can lead to significant health issues. In this project, we aim to investigate the application of machine learning algorithms in predicting diabetes among patients based on their clinical data. Our dataset comprises diverse sources, including electronic health records, medical databases, and surveys. Rigorous preprocessing techniques have been employed to handle data quality, while feature engineering methodologies have been implemented to extract pertinent information. By undertaking these steps, we strive to produce original work without any instances of plagiarism.