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Sabarinath U S and Ashly Mathew. "Medical Insurance Cost Prediction." Indian Journal of Data Communication and Networking 4, no. 4 (June 30, 2024): 1–4. http://dx.doi.org/10.54105/ijdcn.d5037.04040624.
Повний текст джерелаАнотація:
This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. To predict things that have never been so easy. In this project used to predict values that wonder how Insurance amount is normally charged. This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. This project on predicting medical insurance costs can serve various purposes and address several needs that are Accurate Pricing Insurance companies need accurate predictions of medical insurance costs to set appropriate premiums for policyholders. Predictive models can analyse historical data and various factors such as age, gender, pre-existing conditions, lifestyle habits, and geographic location to estimate future healthcare expenses accurately. This Prediction model achieves three regression methods accuracy that the linear regression gets an accuracy of 74.45 %, whereas Ridge regression and Support Vector Regression gets 82.59% word-level state-of-the-art accuracy. The Medical Insurance Cost Prediction project, proposes a comprehensive approach to predict the medical cost, aiming to develop a robust and accurate system capable of predicting the accurate cost for a particular individual. Leveraging linear regression, our proposed system builds upon the successes of existing models like different types of regressions like linear regression, Ridge regression and Support Vector regression. We will put the Regression algorithm into practice and evaluate how it performs in comparison to the other three algorithms. By comparing the performance of these three methodologies, this project aims to identify the most effective approach for medical insurance cost prediction. Through rigorous evaluation and validation processes, the selected model will provide valuable insights for insurance companies, policymakers, and individuals seeking to optimize healthcare resource allocation and financial planning strategies.
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Liu, Enwu, Ryan Yan Liu, and Karen Lim. "Using the Weibull Accelerated Failure Time Regression Model to Predict Time to Health Events." Applied Sciences 13, no. 24 (December 6, 2023): 13041. http://dx.doi.org/10.3390/app132413041.
Повний текст джерелаАнотація:
Clinical prediction models are commonly utilized in clinical practice to screen high-risk patients. This enables healthcare professionals to initiate interventions aimed at delaying or preventing adverse medical events. Nevertheless, the majority of these models focus on calculating probabilities or risk scores for medical events. This information can pose challenges for patients to comprehend, potentially causing delays in their treatment decision-making process. Our paper presents a statistical methodology and protocol for the utilization of a Weibull accelerated failure time (AFT) model in predicting the time until a health-related event occurs. While this prediction technique is widely employed in engineering reliability studies, it is rarely applied to medical predictions, particularly in the context of predicting survival time. Furthermore, we offer a practical demonstration of the implementation of this prediction method using a publicly available dataset.
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Ramesh, Banoth, G. Srinivas, P. Ram Praneeth Reddy, M. D. Huraib Rasool, Divya Rawat, and Madhulita Sundaray. "Feasible Prediction of Multiple Diseases using Machine Learning." E3S Web of Conferences 430 (2023): 01051. http://dx.doi.org/10.1051/e3sconf/202343001051.
Повний текст джерелаАнотація:
Automated Multiple Disease Prediction System using Machine Learning is an advanced healthcare application that utilizes machine learning algorithms to accurately predict the likelihood of a patient having multiple diseases based on their medical history and symptoms. The system employs a comprehensive dataset of medical records and symptoms of various diseases, which are then analysed using machine learning techniques such as decision trees, support vector machines, and random forests. The system’s predictions are highly accurate, and it can assist medical professionals in making more informed decisions and providing better treatment plans for patients. Ultimately, the viable Multiple Disease Prediction System using Machine Learning has the potential to improve healthcare outcomes and reduce healthcare costs by predicting and preventing disease early.
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Kannan, S., G. Premalatha, M. Jamuna Rani, D. Jayakumar, P. Senthil, S. Palanivelrajan, S. Devi, and Kibebe Sahile. "Effective Evaluation of Medical Images Using Artificial Intelligence Techniques." Computational Intelligence and Neuroscience 2022 (August 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/8419308.
Повний текст джерелаАнотація:
This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson’s disease, the affected patients with Parkinson’s disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The presented seizure prediction system introduces deep learning algorithms into EEG score analysis. This proposed work long short-term memory (LSTM) network model is mainly implemented for the identification and classification of qualitative patterns in the EEG of patients. While compared with other techniques like deep learning models such as convolutional neural networks (CNNs) and traditional machine learning algorithms, the proposed LSTM model plays a significant role in predicting impending crises over 4 different qualifying intervals from 10 minutes to 1.5 hours with very few wrong predictions.
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P., Renukadevi. "Lossless Medical Image Compression by Multi Oriented Prediction Technique." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 1277–96. http://dx.doi.org/10.37200/ijpr/v24i5/pr201800.
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Prof. M. S. Patil, Kulkarni Sanika, and Khurpe Sanjana. "MEDICAL INSURANCE PREMIUM PREDICTION WITH MACHINE LEARNING." International Journal of Innovations in Engineering Research and Technology 11, no. 5 (May 18, 2024): 5–11. http://dx.doi.org/10.26662/ijiert.v11i5.pp5-11.
Повний текст джерелаАнотація:
A machine learning method for predicting health insurance rates is presented in this article. With healthcare expenditures becoming more complex, it is critical for insurance companies and policyholders to accurately estimate insurance prices. Utilizing a dataset that included medical history, demographic data, and other pertinent variables, a variety of machine learning techniques, such as ensemble methods and regression, were used to create prediction models. R-Squared and mean absolute error were two measures used to assess these models' performance. According to the developed models' results, insurance premiums can be predicted with accuracy, offering useful information for insurance counteragents. This approach has the potential to optimize pricing strategies, enhance risk assessment, and improve decision-making in the healthcare insurance sector. Machine Learning-Based Prediction of Medical Insurance Premiums Make predictions about health insurance companies based on personal traits. A dataset of policyholder attributes (such as age, gender, BMI, number of children, smoking behaviors, and geography) was gathered and preprocessed .Divide the data into sets for testing and training. Create and train a model for an artificial neural network with TensorFlow and Karas. R-squared metrics and mean R-squared error were used to assess the performance of the model. created a high R-Squared predictive model that was accurate. determined the main determinants of insurance rates. Machine learning has shown promise in estimating healthcare costs. This experiment demonstrates how well machine learning predicts medical insurance rates. Insurance companies may offer more individualized insurance plans, expedite the underwriting process, and help customers make well-informed decisions about their healthcare coverage by creating these predictive models. The created model can help policyholders make educated judgments and insurance companies establish proper prices. In the long run, our research helps the insurance industry enhance data-driven techniques, which benefits insurers as well as insured individuals in general.
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Ben Shoham, Ofir, and Nadav Rappoport. "CPLLM: Clinical prediction with large language models." PLOS Digital Health 3, no. 12 (December 6, 2024): e0000680. https://doi.org/10.1371/journal.pdig.0000680.
Повний текст джерелаАнотація:
We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical disease and readmission. We utilized quantization and fine-tuned the LLM using prompts. For diagnostic predictions, we predicted whether patients would be diagnosed with a target disease during their next visit or in the subsequent diagnosis, leveraging their historical medical records. We compared our results to various baselines, including Retain and Med-BERT, the latter of which is the current state-of-the-art model for disease prediction using temporal structured EHR data. In addition, we also evaluated CPLLM’s utility in predicting hospital readmission and compared our method’s performance with benchmark baselines. Our experiments ultimately revealed that our proposed method, CPLLM, surpasses all the tested models in terms of PR-AUC and ROC-AUC metrics, providing state-of-the-art performance as a tool for predicting disease diagnosis and patient hospital readmission without requiring pre-training on medical data. Such a method can be easily implemented and integrated into the clinical workflow to help care providers plan next steps for their patients.
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Takke, Kunal, Rameez Bhaijee, Avanish Singh, and Mr Abhay Patil. "Medical Disease Prediction using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 221–27. http://dx.doi.org/10.22214/ijraset.2022.42135.
Повний текст джерелаАнотація:
Abstract: There is a growing importance of healthcare and pandemic has proved that healthcare is an important aspect of an individual life. Most of the medical diagnoses require going to the doctor and fixing appointments for a consultation and sometimes to get accurate disease indications we have to wait for blood reports also we have to travel long distances to seek doctor consultation. When we are not feeling well the first thing we do is to check our temperature to get an estimate or baseline idea of our fever so we can consult our doctor if the temperature is high enough similarly a medical disease prediction application can be used to get a baseline idea of disease and can indicate us whether we should take immediate doctor consultation or not, or at least start some home-remedies for the same to find temporary relief. Combining machine learning with an application interface to interact with users provides opportunities for easy interaction with the users with the machine learning model to get more accurate predictions. Sometimes people feel reluctant to visit a hospital or consult a doctor for minor symptoms but there are cases where these minor symptoms may be indications of severe health problems hence medical disease prediction maybe useful to get a baseline prediction or estimation of disease in such cases. Keywords: machine Learning, prediction, medical diagnosis, healthcare
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R, Ashwini, S. M. Aiesha Afshin, Kavya V, and Prof Deepthi Raj. "Diabetes Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 544–49. http://dx.doi.org/10.22214/ijraset.2022.41143.
Повний текст джерелаАнотація:
Abstract: The concept of machine learning has quickly become very attractive to the healthcare industry. Predictions and analyzes made by the research community on medical data sets help with appropriate care and precautions in the prevention of disease. of machine learning, the types of algorithms that can help make decisions and predictions. We also discuss various applications of machine learning in the medical field, with a focus on diabetes prediction through machine learning. Diabetes is one of the most increasing diseases in the world and it requires continuous monitoring. To check this, we explore various machine learning algorithms which will help in early prediction of this disease. This work explains various aspects of machine learning, the types of algorithm which can help in decision making and prediction. The predictions and analysis made by the research community for medical dataset support the people by taking proper care and precautions by preventing diseases. Discuss various applications of machine learning in the field of medicine focusing on the prediction of diabetes through machine learning. Diabetes is one of the fastest-growing diseases in the world and requires constant monitoring. To verify this, we are exploring different machine learning algorithms that will help with this baseline prediction. Keywords: Decision Support Systems, Diabetes, Machine learning, Support vector Machine, Random Forest, K-Nearest Neighbor, Logistics Regression
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Reiz, Beáta, and Lehel Csató. "Bayesian Network Classifier for Medical Data Analysis." International Journal of Computers Communications & Control 4, no. 1 (March 1, 2009): 65. http://dx.doi.org/10.15837/ijccc.2009.1.2414.
Повний текст джерелаАнотація:
<p>Bayesian networks encode causal relations between variables using probability and graph theory. They can be used both for prediction of an outcome and interpretation of predictions based on the encoded causal relations. In this paper we analyse a tree-like Bayesian network learning algorithm optimised for classification of data and we give solutions to the interpretation and analysis of predictions. The classification of logical – i.e. binary – data arises specifically in the field of medical diagnosis, where we have to predict the survival chance based on different types of medical observations or we must select the most relevant cause corresponding again to a given patient record.<br />Surgery survival prediction was examined with the algorithm. Bypass surgery survival chance must be computed for a given patient, having a data-set of 66 medical examinations for 313 patients.</p>
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Severn, Cameron, Krithika Suresh, Carsten Görg, Yoon Seong Choi, Rajan Jain, and Debashis Ghosh. "A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features." Sensors 22, no. 14 (July 12, 2022): 5205. http://dx.doi.org/10.3390/s22145205.
Повний текст джерелаАнотація:
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as “black boxes”. Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.
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Ahuja, Teesha. "Employability of the Machine Learning Algorithms in the Early Diagnosis of Various Diseases." International Journal of Research in Medical Sciences and Technology 13, no. 01 (2022): 158–63. http://dx.doi.org/10.37648/ijrmst.v13i01.015.
Повний текст джерелаАнотація:
The most difficult task is accurately predicting disease. Environment and lifestyle factors contribute to a wide range of illnesses. As a result, it becomes a crucial task to predict disease earlier. On the other hand, the doctor finds it too difficult to predict symptoms accurately. Predicting the disease is important in using data mining to solve this issue. Medical science experiences significant annual data growth. Early patient care has benefited from accurate medical data analysis because of the growing amount of data in the medical field. Data mining uncovers hidden pattern information in a wide range of medical data by utilizing disease data. Based on the patient's symptoms, we proposed a general disease prediction. We use the machine learning algorithms K-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN) for accurate disease prediction. A dataset of disease symptoms was required for disease prediction. A person's lifestyle and checkup information are considered for an accurate prediction in this general disease prediction. CNN has a higher general disease prediction accuracy of 84.5% than the KNN algorithm. Additionally, KNN's memory and time requirements are higher than CNN's. This system can provide the risk associated with the prevalent disease, which can be either a lower or higher risk of the prevalent disease after general disease prediction.
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Supriya, M., and A. J. Deepa. "A Survey on Prediction Using Big Data Analytics." International Journal of Big Data and Analytics in Healthcare 2, no. 1 (January 2017): 1–15. http://dx.doi.org/10.4018/ijbdah.2017010101.
Повний текст джерелаАнотація:
This article describes how nowadays, the growth of big data in bio-medical and healthcare community services is increasing rapidly. The early detection of diseases and patient care are analyzed with the help of accurate analysis of medical data includes diagnosed patients' details. The analysis of accuracy rate is considerably reduced when the quality of medical data is unclear since every part of the body has unique characteristics of certain regional diseases that may suppress the prediction of diseases. This article reviews the detailed survey of different prediction methods developed for analyzing the accuracy rate of disease affected patients in 2015-2016 mainly focuses on choosing the efficient predictions based on the quality of medical data not only provides the overall view of prediction methods but also gives the idea of big data analytics in medical data further discusses the methods, techniques used and the pros and cons of prediction methods.
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Azmi, Fadhillah, and Amir Saleh. "A Hybrid Algorithm for Multiple Disease Prediction: Radial Basis Function and Logistic Regression." International Journal of Science and Healthcare Research 9, no. 2 (July 1, 2024): 363–68. http://dx.doi.org/10.52403/ijshr.20240246.
Повний текст джерелаАнотація:
Disease prediction is an important aspect of modern medicine, which aims to diagnose disease early and provide appropriate treatment to patients. This research uses a hybrid approach that combines the RBF (Radial Basis Function) kernel algorithm with logistic regression to predict various diseases in medical datasets. This method is intended to improve prediction performance by exploiting the advantages of each algorithm. This research uses a dataset containing medical information about several diseases collected from the Kaggle dataset. First, the RBF kernel is applied to transform the data features into a more informative, non-linear representation. Then, the logistic regression model is used to make predictions based on the features that have been processed by the RBF kernel. In this research, the hybrid RBF (Radial Basis Function) method was proven to be superior in predicting multiple diseases. This method shows the highest accuracy of 0.9460, as well as excellent precision, recall, and F1-score values of 0.8680, 0.8097, and 0.8294, respectively. The advantage of the hybrid RBF method lies in its ability to capture complex patterns in data that other methods often cannot identify, as well as its ability to handle non-linear decision boundaries, which are a common characteristic in medical datasets. Keywords: Disease prediction, Hybrid approach, RBF kernel algorithm, Logistic regression, medical datasets
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Prazdnikova, Margaryta. "Prediction and Assessment of Myocardial Infarction Risk on the Base of Medical Report Text Collection." Cybernetics and Computer Technologies, no. 4 (December 18, 2024): 71–80. https://doi.org/10.34229/2707-451x.24.4.7.
Повний текст джерелаАнотація:
Introduction. Myocardial infarction remains one of the leading causes of death worldwide, resulting from sudden disruption of blood supply to the heart muscle. Key risk factors include smoking, age, gender, high cholesterol levels, diabetes, and others. Despite advancements in diagnostics and treatment, early detection of heart attack risk is crucial for reducing mortality and improving patient quality of life. This paper explores an approach to predicting heart attack risk based on analysis of text data of medical reports using machine learning. The purpose of the article is to demonstrate how the application of machine learning, particularly the Naive Bayes classifier, can enhance the prediction of myocardial infarction risk through the analysis of extensive medical data. By leveraging a depersonalized database from SSO CITHC SAA, containing medical records collected during a decade of operating, this study seeks to reveal how the identification of critical patterns and factors can improve prediction accuracy. Additionally, the article explores how integrating these predictive models into clinical decision support systems can refine medical diagnostics and decision-making processes. Results. The proposed prediction model demonstrated high efficiency in identifying patients at increased risk of heart attack. By analyzing the frequency of specific words in medical records, the algorithm successfully predicted a high risk of heart attack for 80 % of patients with an expected event. This underscores the significant potential of leveraging textual data and machine learning methods in medical diagnostics. Moreover, the reduction in false predictions highlights the model's reliability and suitability for practical application. Conclusions. Employing machine learning for heart attack risk prediction based on medical data analysis represents a promising direction in modern medicine. The developed model showcases the possibility of enhancing diagnostic and predictive accuracy, which can substantially influence treatment strategy decisions and improve patient outcomes. Integrating such tools into clinical practice will facilitate more informed decisions by physicians and reduce patient risks. Keywords: myocardial infarction, risk prediction, machine learning, database, Naive Bayes classifier, medical analytics.
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Perepeka, Eugene, Vasyl Lazoryshynets, Vitalii Babenko, Illia Davydovych, and Ievgen Nastenko. "Cardiomyopathy prediction in patients with permanent ventricular pacing using machine learning methods." System research and information technologies, no. 1 (March 29, 2024): 33–41. http://dx.doi.org/10.20535/srit.2308-8893.2024.1.03.
Повний текст джерелаАнотація:
Pacing-induced cardiomyopathy is a notable issue in patients needing permanent ventricular pacing. Identifying risk groups early and swiftly preventing the ailment can reduce patient harm. However, current prognostic methods require clarity. We employed machine learning to develop predictive models using medical data. Three algorithms — decision tree, group method of data handling, and logistic regression — formed models that forecast pacing-induced cardiomyopathy. These models displayed high accuracy in predicting development, signifying soundness. Factors like age, paced QRS width, pacing mode, and ventricular index during implantation significantly influenced predictions. Machine learning can enhance pacing-induced cardiomyopathy prediction in ventricular pacing patients, aiding medical practice and preventive strategies.
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Mankar, Aayush, Atharv Pawar, Akash Pore, and Amar Waghmare. "Different Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 1907–10. http://dx.doi.org/10.22214/ijraset.2023.56897.
Повний текст джерелаАнотація:
Abstract: One of the most significant subjects of society is human healthcare. It is looking for the best one and robust disease diagnosis to get the care they need as soon as possible.The task of following new approaches is challenging these disciplines, moving beyond the conventional ones.The actual number of new techniques makes it possible to provide a broad overview that avoids particular aspects. To this end, we suggest a systematic analysis of human diseases related to machine learning. This research concentrates on existing techniques related to machine learning growth applied to the diagnosis of human illnesses in the medical field to discover exciting trends, make unimportant predictions, and help decision-making.This paper analyzes unique machine learning algorithms used for healthcare applications to create adequate decision support.This paper intends to reduce the research gap in creating a realistic decision support system for medical applications. .Datasets are used in this project for predictions. Random Forest algorithm is to create multiple decision trees during training and then combine their predictions to obtain a more accurate and robust model. Ensemble models based on deep learning have made significant contributions to the medical field, particularly in the area of disease prediction. Breast cancer is a highly aggressive disease with a high mortality rate. Timely and effective prediction of breast cancer can reduce the risk of it progressing to later stages and the need for unnecessary medications. This study proposes a novel machine learning algorithm to predict the AD progression utilising a multi-task ensemble learning approach. Specifically, we present a novel tensor multi-task learning (MTL) algorithm based on similarity measurement of spatio-temporal variability of brain biomarkers to model AD progression. In this model, the prediction of each patient sample in the tensor is set as one task, where all tasks share a set of latent factors obtained through tensor decomposition. Furthermore, as subjects have continuous records of brain biomarker testing, the model is extended to ensemble the subjects’ temporally continuous prediction results utilising a gradient boosting kernel to find more accurate predictions
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Tian, Mohan, Yingci Li, and Hong Chen. "18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival." Contrast Media & Molecular Imaging 2023 (May 4, 2023): 1–10. http://dx.doi.org/10.1155/2023/2986379.
Повний текст джерелаАнотація:
Colon cancer is a type of cancer that begins in the large intestine. In the process of efficacy evaluation, postoperative recurrence prediction and metastasis monitoring of colon cancer, traditional medical image analysis methods are highly dependent on the personal ability of the doctors. In the process of patient treatment, it not only increases the workload and work pressure for doctors, but also has some problems with traditional medical image analysis methods. Moreover, the traditional medical image analysis methods have problems such as insufficient prediction accuracy, slow prediction speed, and the risk of errors in prediction. When analyzing 18F-FDG PET/CT images by traditional medical image analysis methods, it is easy to cause problems such as untimely treatment plans and errors in diagnosis, which will adversely affect the survival of colon cancer patients. Although 18F-FDG PET/CT images have certain advantages in image clarity and accuracy compared with traditional medical imaging methods, the analysis method based on 18F-FDG PET/CT images also has certain effects in predicting the survival of colon cancer patients, but there are still many shortcomings: the 18F-FDG PET/CT image analysis method overly relies on the technical advantages of 8F-FDG PET/CT images; in the analysis and prediction of image data, it has not gotten rid of the dependence on the personal medical quality of the doctors; traditional medical image analysis methods are still used when analyzing and predicting images; there is no breakthrough in image analysis effects. In order to solve these problems, this paper combined deep learning theory, using three algorithms of the improved RBM algorithm, image feature extraction method based on deep learning, and regression neural network to analyze and predict 18F-FDG PET/CT images, and applied some algorithms to analyze and predict 18F-FDG PET/CT images, and also established a deep learning-based 18F-FDG PET/CT image survival analysis prediction model. Four aspects survival prediction accuracy, survival prediction speed, survival prediction precision, and physician satisfaction were studied through this model. The research results have shown that compared with traditional medical image analysis methods, the prediction accuracy of 18F-FDG PET/CT image survival analysis prediction model based on deep learning is improved by 0.83%, and the prediction speed is improved by 3.42%, as well as the prediction precision increased by 6.13%. The research results show that the deep learning-based 18F-FDG PET/CT image survival analysis prediction model established in this paper is of great significance to improve the survival rate of colon cancer patients, and also promotes the development of the medical industry.
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Liu, Laura. "Disease Prediction Models Based on Medical Big Data." Theoretical and Natural Science 63, no. 1 (November 22, 2024): 139–43. http://dx.doi.org/10.54254/2753-8818/2024.17942.
Повний текст джерелаАнотація:
The advent of big data technology has heralded a transformative era in healthcare, with significant implications for disease prediction. This review article delves into the integration of medical big data in predictive modeling, highlighting the pivotal role of data preprocessing, feature engineering, and machine learning algorithms. We explore the escalating research interest, as evidenced by an upward trend in academic publications from 2010 to 2023. The paper underscores the advantages of big data analytics in healthcare, leading to more accurate and personalized disease predictions. Furthermore, we discuss the importance of interdisciplinary collaboration between data scientists, clinicians, and bioinformaticians in enhancing predictive modeling.
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Liu, Chang Chun, Tao Wu, and Cheng He. "State of health prediction of medical lithium batteries based on multi-scale decomposition and deep learning." Advances in Mechanical Engineering 12, no. 5 (May 2020): 168781402092320. http://dx.doi.org/10.1177/1687814020923202.
Повний текст джерелаАнотація:
To guarantee rescue time and reduce medical accidents, a health degradation prediction model of medical lithium-ion batteries based on multi-scale deep neural network was proposed aiming at the problems of poor model adaptability and inaccurate prediction in current state of health prediction methods. The collected energy data of medical lithium-ion batteries were decomposed into main trend data and fluctuation data by ensemble empirical mode decomposition and correlation analysis. Then, deep Boltzmann machines and long short-term memory were used to model the main trend and fluctuation data, respectively. The predicting outcomes of deep Boltzmann machines and long short-term memory were effectively integrated to obtain the health predicted results of medical lithium-ion battery. The experimental results show that the method can effectively fit the health trend of medical lithium-ion batteries and obtain accurate state of health prediction results. The performance of the method is better than other typical prediction methods.
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Alamelu, J. V., and Mythili Asaithambi. "EVALUATION OF MEDICAL GRADE INFUSION PUMP PARAMETER USING GAUSSIAN PROCESS REGRESSION." Biomedical Sciences Instrumentation 58, no. 2 (April 15, 2022): 59–66. http://dx.doi.org/10.34107/nsjx733559.
Повний текст джерелаАнотація:
Prediction techniques are extensively used in medical applications and health care devices. The prediction of the infusion flow rate for the required drug dosage and drug concentration in a smart wireless infusion pump is necessary for precise drug flow for the patients. In this paper, the prediction model has been developed to predict the lag time using Gaussian Process Regression (GPR) technique with a squared exponential kernel. Currently, a smart wireless infusion pump is incorporated with its smart drug library. The required parameters such as drug dosage, drug flow rate are utilized as inputs to predict the lag time and to minimize start-up delays using the proposed regression technique. The evaluation of the prediction model is done by the coefficient of determination (R2), mean absolute error (MAE), and root-mean-squared error (RMSE). These prediction results are verified for predicting lag time for two different carrier flowrates 10 ml/hr and 50 ml/hr. The outcome of the study indicates that the regression model GPR has better prediction accuracy with a mean R2 of 0.9. Hence, the GPR technique is capable to achieve quick infusion and optimal flow rate with minimized lag time for smart infusion pumps.
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Alamelu, J. V., and A. Mythili. "Evaluation of medical grade infusion pump parameters using Gaussian Process Regression." EAI Endorsed Transactions on Pervasive Health and Technology 8, no. 5 (November 25, 2022): e3. http://dx.doi.org/10.4108/eetpht.v8i5.3171.
Повний текст джерелаАнотація:
Prediction techniques are extensively used in medical applications and health devices. The prediction of the infusion flow rate and its speed in a smart wireless infusion pump is necessary to provide precise drug flow. This paper has developed the prediction model to predict the lag time and infusion pump speed using the Gaussian process regression (GPR) technique with a squared exponential kernel. The present smart wireless infusion pump is usually incorporated with its smart drug library. The required parameters such as drug dosage, drug flow rate are utilized as inputs to predict the pump speed, minimize start-up delays using proposed regression techniques. The evaluation of prediction models is done by the coefficient of determination (R2), mean absolute error (MAE), and root-mean-squared error (RMSE). These prediction results are verified for predicting lag time and infusion pump speed for two different carrier flowrates, 10 ml/hr, 50 ml/hr. The study's outcome indicates that the regression model GPR has better prediction accuracy with a mean coefficient of determination of 0.99. Hence, the GPR technique can achieve quick infusion speed with minimized lag time,the optimal flow rate for smart infusion pumps.
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Melchane, Selestine, Youssef Elmir, Farid Kacimi, and Larbi Boubchir. "Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review." Acta Universitatis Sapientiae, Informatica 16, no. 1 (January 8, 2025): 160–97. https://doi.org/10.47745/ausi-2024-0010.
Повний текст джерелаАнотація:
Artificial Intelligence and infectious diseases prediction have recently experienced a common development and advancement. Machine learning apparition, along with deep learning emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accomplished, but also to highlight the difficulties related to the types of data used, and the learning methods applied for each research objective. It categorizes these contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients’ Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population. The paper also critically assesses the potential of Artificial Intelligence and outlines its limitations in infectious disease management.
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Setyonugroho, Winny, Sentagi Sesotya, Iman Permana, Tri Lestari, Didit Mahendra, and Habib Abda. "Enhancing Predictive Accuracy: Assessing the Effectiveness of SVM in Predicting Medical Student Performance." E3S Web of Conferences 465 (2023): 02028. http://dx.doi.org/10.1051/e3sconf/202346502028.
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The high cost of pursuing a medical education necessitates effectively monitoring and evaluating medical students' performance. This study aimed to develop and evaluate a prediction system for medical students’ national exam scores using the Support Vector Machine (SVM) algorithm. The dataset consisted of grades from first and second-year medical students at Muhammadiyah University of Yogyakarta, specifically from the 2014 and 2015 classes, to predict the final year exam score. The methodology involved data acquisition, data preprocessing, and classification and prediction of student performance. Remarkably, the SVM model achieved an accuracy rate of 95.48%. The findings highlight the substantial potential of SVM for accurately predicting medical student performance. The prediction system can enable educational institutions to proactively identify students needing additional support or intervention. This early intervention can help improve academic progress and enhance the overall quality of medical education. Future research efforts should focus on improving the prediction system's practicality and effectiveness by incorporating additional factors. This study successfully developed and evaluated a prediction system for medical student performance using the SVM algorithm. The high accuracy achieved by the SVM model emphasises its potential as a valuable tool for medical education institutions. By leveraging machine learning, educational institutions can provide targeted support to students, leading to improved learning outcomes and advancements in medical education.
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Wade, Bruce A., Krishnendu Ghosh, and Peter J. Tonellato. "Optimization of a Gene Analysis Application." Computing Letters 2, no. 1-2 (March 6, 2006): 81–88. http://dx.doi.org/10.1163/157404006777491927.
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MetaGene is a software environment for gene analysis developed at the Bioinformatics Research Center, Medical College of Wisconsin. In this work, a new neural network optimization module is developed to enhance the prediction of gene features developed by MetaGene. The input of the neural network consists of gene feature predictions from several gene analysis engines used by MetaGene. When compared, these predictions are often in conflict. The output from the neural net is a synthesis of these individual predictions taking into account the degree of conflict detected. This optimized prediction provides a more accurate answer when compared to the default prediction of MetaGene or any single prediction engine’s solution.
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Anilkumar, Chunduru, Seepana Kanchana, Sasapu Bharath Kumar, Reddy Pravallika, and Surapureddi Mrudula. "Multi chronic disease prediction: A survey." Applied and Computational Engineering 5, no. 1 (June 14, 2023): 273–78. http://dx.doi.org/10.54254/2755-2721/5/20230579.
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People today deal with a variety of illnesses as a result of their lifestyle choices and the environment. As a result, many people have chronic diseases that go untreated for long periods of time, imposing a tremendous impact on society. Therefore, predicting disease sooner is becoming a crucial duty. in order to systematically evaluate patients' future disease risks using their medical records. But for a doctor, making an accurate forecast based on symptoms is too challenging. The hardest task is making an accurate diagnosis of a condition. For this problem to be resolved, illness detection requires the use of deep learning and machine learning approaches. The amount of data in the medical sciences grows significantly every year. Earlier, health care for patient care has benefited from precise medical data analysis due of the development of information in the medical and healthcare areas. Prior identification and therapy are usually necessary to prevent chronic aeropathy from getting worse. Machine learning and deep learning algorithms are used to predict chronic diseases. Eight illness categories were our predictions. The Random Forest ensemble learning approach fared best overall. Finding the sickness prediction techniques with the highest accuracy and computation efficiency is the aim of this study.
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Lu, Yi. "Heart Disease Prediction Model based on Prophet." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 1035–40. http://dx.doi.org/10.54097/hset.v39i.6700.
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Heart disease is one of the major causes of death for people of all races, genders, and nationalities. In the United States, for instance, heart disease causes more than 600,000 deaths every year and is the largest leading cause of death in 2020. A reliable heart diseases mortality prediction model could acknowledge the patients’ medical professionals that the heart disease risk level of the specific group. This approach is significant in preventing further increases in heart disease mortality rates worldwide. Nowadays, multiple Machine Learning (ML) models, including hybrid models produced impressive predictions and realized that newly developed ML models might provide new perspectives on heart disease predictions. In this paper, we introduced the Facebook Prophet model (FB Prophet model), a time series prediction tool that could present seasonality in its result, since studies point out that heart disease mortality rate also shows seasonality. We produced an accuracy of approximately 94 % in predicting weekly heart disease mortality numbers in specific states. Furthermore, we explored the effects that external factors, ambient temperature, have on heart disease, and utilize this relationship in improving model accuracy.
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Kulkarni, Mukund, Dhammadeep D. Meshram, Bhagyesh Patil, Rahul More, Mridul Sharma, and Pravin Patange. "Medical Insurance Cost Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 449–56. http://dx.doi.org/10.22214/ijraset.2022.47923.
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Abstract: Insurance is a policy that helps to cover up all loss or decrease loss in terms of expenses incurred by various risks. A number of variables affect how much insurance costs. These considerations of different factors contribute to the insurance policy cost expression. Machine Learning( ML) in the insurance sector can make insurance more effective. In the domains of computational and applied mathematics the machine learning (ML) is a well-known research area. ML is one of the computational intelligence aspects when it comes to exploitation of historical data that may be addressed in a wide range of applications and systems. There are some limitations in ML so; Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry and thus it requires few more investigation and improvement. Using the machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. The proposed research approach uses Linear Regression, Decision Tree Regression and Gradient Boosting Regression and also streamlit as a framework. We had used a medical insurance cost dataset that was acquired from the KAGGLE repository for the cost prediction purpose, and machine learning methods are used to show the forecasting of insurance costs by regression model comparing their accuracies
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Pratama, Matthew. "Utilizing Linear Regression for Predicting Sales of Top-Performing Products." International Journal of Information Technology and Computer Science Applications 1, no. 3 (September 10, 2023): 174–80. http://dx.doi.org/10.58776/ijitcsa.v1i3.92.
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PT Ajidarma Delta Medika is a company engaged in the sale of medical devices in the city of Bekasi. This company markets a variety of medical device products. Judging from the large number of consumer requests for medical device products based on sales data for the last 3 years, predictions are needed for the best-selling product sales, in order to facilitate the company in planning the supply of stock. To find out the best-selling medical device product sales, data prediction techniques are used with the Linear Regression algorithm. By using the Linear Regression algorithm, the results are obtained to predict the best-selling sales of several products sold at PT Ajidarma Delta Medika. This research produces an accuracy value with the MAPE formula for predicting the best-selling product sales of 14.2%. This shows that the linear regression method is good at predicting sales of medical devices in the following year.
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Csillag, Daniel, Lucas Monteiro Paes, Thiago Ramos, João Vitor Romano, Rodrigo Schuller, Roberto B. Seixas, Roberto I. Oliveira, and Paulo Orenstein. "AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15494–502. http://dx.doi.org/10.1609/aaai.v37i13.26837.
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Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages deep learning and conformal prediction to output fast and accurate volume estimates and segmentation masks from fetal MRIs with Dice coefficient over 0.9. Also, we make available a novel, curated dataset for fetal MRIs with 853 exams and benchmark the performance of many recent deep learning architectures. In addition, we introduce a conformal prediction tool that yields narrow predictive intervals with theoretically guaranteed coverage, thus aiding doctors in detecting pregnancy risks and saving lives. A successful case study of AmnioML deployed in a medical setting is also reported. Real-world clinical benefits include up to 20x segmentation time reduction, with most segmentations deemed by doctors as not needing any further manual refinement. Furthermore, AmnioML's volume predictions were found to be highly accurate in practice, with mean absolute error below 56mL and tight predictive intervals, showcasing its impact in reducing pregnancy complications.
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Md Mohtaseem Billa. "Medical Insurance Price Prediction Using Machine Learning." Journal of Electrical Systems 20, no. 7s (May 4, 2024): 2270–79. http://dx.doi.org/10.52783/jes.3962.
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The escalating costs and complexities in the healthcare sector underscore the necessity for efficient predictive models to anticipate medical insurance prices. This study explores the application of machine learning techniques for forecasting medical insurance premiums, aiming to provide stakeholders with invaluable insights for pricing strategies and risk management. Using a comprehensive dataset encompassing demographic information, medical history, lifestyle factors, and insurance coverage details, various machine learning algorithms including regression, decision trees, random forests are employed and compared. Feature engineering techniques are applied to enhance model performance and interpretability, ensuring the inclusion of relevant predictors while mitigating overfitting. However, in recent years, the emergence of machine learning techniques has offered promising solutions to enhance medical insurance price prediction. This paper conducts an extensive review of various machine learning approaches utilized for this purpose, covering regression-based methods, time series forecasting techniques, ensemble methods, deep learning strategies, and hybrid models. We delve into the unique strengths, limitations, and practical applications of each technique. Moreover, we address the prevalent challenges associated with employing machine learning in medical insurance price prediction, such as data accessibility, feature selection, model interpretability, scalability, and generalization. Additionally, we look ahead to future research avenues and opportunities aimed at refining the accuracy and utility of machine learning models in predicting insurance prices. Through this comprehensive review, we aim to provide valuable insights for researchers, practitioners, and policymakers, facilitating informed decision-making in healthcare contexts through the utilization of machine learning methodologies.
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Elam, C. L., та M. M. Johnson. "Prediction of medical studentsʼ academic performances". Academic Medicine 67, № 10 (жовтень 1992): S28–30. http://dx.doi.org/10.1097/00001888-199210000-00029.
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van Houwelingen, Hans. "Special Issue: Prediction in Medical Statistics." Statistica Neerlandica 55, no. 1 (March 2001): 2. http://dx.doi.org/10.1111/1467-9574.00152.
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Nouretdinov, Ilia, Dmitry Devetyarov, Volodya Vovk, Brian Burford, Stephane Camuzeaux, Aleksandra Gentry-Maharaj, Ali Tiss, et al. "Multiprobabilistic prediction in early medical diagnoses." Annals of Mathematics and Artificial Intelligence 74, no. 1-2 (July 12, 2013): 203–22. http://dx.doi.org/10.1007/s10472-013-9367-5.
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Lei, Qiyun. "Machine Learning in Medical Insurance Prediction." Advances in Economics, Management and Political Sciences 45, no. 1 (December 1, 2023): 222–28. http://dx.doi.org/10.54254/2754-1169/45/20230270.
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Nowadays, the trend of an ageing society is more and more obvious. Accompanied with the huge population of the elderly, the medical insurance industry has more prospects and potential. As a result, more service and business operations of insurance companies are in need. With the analysis from past data, computer algorithms help a lot in predicting the new output values, aiding data-driven business decisions, ranking of influential factors and digital computerization. Through machine learning, the insurance companies are able to make a decision flatly in premiums without having unnecessary medical expenditure. The provided models include linear regression, polynomial regression, and random forest. Through the comparation of these three models, with the output data of MAE and other indicators, we can see that polynomial regression is the best model. Within the efficient operation of this method, it can soon be prevalent among the medical industry. Avoiding problems of high cost of labor and inevitable manmade mistakes, polynomial regression aids the technical advance and statistical progress to prosper.
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J., Sirisha,. "LUNG CANCER PREDICTION THROUGH DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 3, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29970.
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Lung cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the critical need for accurate prediction and early detection methods. In recent years, Convolutional Neural Network (CNN) models have emerged as powerful tools for medical image analysis, showing promising results in various diagnostic tasks. Building upon previous research utilizing Artificial Neural Network (ANN) models, this paper presents an in-depth investigation into the application of CNN models for lung cancer prediction using medical imaging data. Leveraging insights from previous ANN-based approaches, we propose novel CNN architectures and explore advanced techniques to enhance predictive performance. Through rigorous experimentation and evaluation, our study demonstrates the effectiveness of CNN models in accurately predicting lung cancer from computed tomography (CT) scan images. We also discuss the potential clinical implications and future directions for leveraging deep learning methods in lung cancer prediction and diagnosis. Keywords: Lung cancer prediction, Convolutional Neural Networks, Deep Learning, Medical Imaging, Computed Tomography.
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Hatoum, Rima, Ali Alkhazraji, Zein Al Abidin Ibrahim, Houssein Dhayni, and Ihab Sbeity. "Towards a disease prediction system: BioBERT-based medical profile representation." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (June 1, 2024): 2314. http://dx.doi.org/10.11591/ijai.v13.i2.pp2314-2322.
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<p>Healthcare professionals are increasingly interested in predicting diseases before they manifest, as this can prevent more serious health conditions and even save lives. Machine learning techniques are now playing an important role in healthcare, including in the early prediction of diseases based on prior medical knowledge. However, one of the biggest challenges is how to represent medical information in a way that can be processed by machine learning algorithms. Medical histories are often in a format that computers cannot read, so filtering and converting this information into numerical representations is a crucial step. This process has become easier with the advancement of natural language processing techniques. In this paper, we propose three representations of medical information, two of which are based on BioBERT, the latest text representation techniques for the biomedical sector. The efficiency of these representations is tested on the MIMIC-III database, which contains information on 46,520 patients. The focus of the study is on predicting Coronary Artery Disease, and the results demonstrate the effectiveness of the proposed approach. The study highlights the importance of medical history in disease prediction and demonstrates the potential of machine learning techniques to advance healthcare.</p>
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Bhavekar, Girish Shrikrushnarao, Pratiksha Vasantrao Chafle, Agam Das Goswami, Ganesh Kumar Marathula, Sumit Arun Hirve, Suraj Rajesh Karpe, Nitin Sonaji Magar, et al. "Hybrid approach to medical decision-making: prediction of heart disease with artificial neural network." Bulletin of Electrical Engineering and Informatics 13, no. 6 (December 1, 2024): 4124–33. http://dx.doi.org/10.11591/eei.v13i6.5583.
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Heart disease prediction is important in today’s world because it helps to reduce the unpredictable death rate of patients, and cardiac diseases are considered one of the most serious diseases affecting people. Hence, in this paper, a heart disease prediction model is designed for effective prediction of heart diseases by means of machine learning (ML) and deep learning (DL). This prediction uses the proposed method of an artificial neutral network and the Chi2 feature selection method applied to determine which features from the dataset were suitable for prediction. The proposed methodology uses classifiers like support vector machines (SVM), Naive Bayes (NB), logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Python was used to conduct the study that assessed the ANN system proposal with the Cleveland heart disease dataset at the University of California (UCI). Compared to other algorithms, the model achieves an accuracy of 97.64% and takes 0.49 seconds to execute, making it superior in predicting heart disease.
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Zhang, Liangqing, Cuirong Yu, Chunrong Jin, Dajin Liu, Zongwen Xing, Qian Li, Zhinan Li, Qin Li, Yingxiao Wu, and Jie Ren. "A Remote Medical Monitoring System for Heart Failure Prognosis." Mobile Information Systems 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/406327.
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Remote monitoring of heart disease provides the means to keep patients under continuous supervision. In this paper, we introduce the design and implementation of a remote monitoring medical system for heart failure prediction and management. The three-part system includes a patient-end for data collection, a medical data center as data storage and analysis, and a doctor-end to diagnosis and intervention. The main objective of the system is to prognose the occurrence risk of heart failure (HF) confirmed by the level of N-terminal prohormone of brain natriuretic peptide (NT-proBNP) based on the changes of the patients’ (systolic and diastolic) blood pressure and body weight that are measured noninvasively in a home environment. The prediction of HF and non-HF patients was achieved by a structured support vector machine (SVM) classification algorithm. With the present system, we also proposed a scoring method to interpret the long-term risk of HF. We demonstrated the efficiency of the system with a pilot clinical study of 34 samples, where the NT-proBNP test was used to help train the prediction model as well as check the prediction results for our system. Results showed an accuracy of 79.4% for predicting HF on day 7 based on daily body weight and blood pressure data acquired over 30 days.
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Rakhmetullina, Zhenisgul, Saule Belginova, Alibekkyzy Karlygash, Aigerim Ismukhamedova, and Shynar Tezekpaeva. "Research and implementation of the medical text analysis algorithm for predicting mortality." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (June 1, 2024): 1965. http://dx.doi.org/10.11591/ijeecs.v34.i3.pp1965-1977.
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Mortality prediction has a role to play in the development of a descriptive measure of the quality of care that provides a fair and equitable means of comparing and evaluating hospitals. This article describes a study of a medical text analysis algorithm for mortality prediction that used big data in the form of unstructured medical notes. The article describes the concept of using text mining technology for medical systems, a method for preprocessing medical data to predict patient mortality, an algorithm for predicting patient deaths based on the logistic regression classifier and presents a software module for implementing the proposed algorithm.
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Spreafico, Marta, Audinga-Dea Hazewinkel, Michiel A. J. van de Sande, Hans Gelderblom, and Marta Fiocco . "Machine Learning versus Cox Models for Predicting Overall Survival in Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data." Cancers 16, no. 16 (August 19, 2024): 2880. http://dx.doi.org/10.3390/cancers16162880.
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Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data.
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Madhuri Thimmapuram, Ananda Rao Akepogu. "Deep Learning: A Future Prognostic Tool in Medical Illness Prediction." Journal of Information Systems Engineering and Management 10, no. 15s (March 4, 2025): 506–27. https://doi.org/10.52783/jisem.v10i15s.2490.
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Artificial intelligence (AI) has a bright future in healthcare, as evidenced by its rapid growth and the gradual onset of AI research in the medical industry in recent years. The prediction of diseases and drugs has showed potential using deep learning. From a logistic regression model, to a machine learning model, and now to a deep learning model, improvements have been made in the ability to accurately predict medical illnesses. In this article, common illnesses, fundamental deep learning frameworks, and deep learning prediction techniques are all introduced to make a future prognosis and draw attention to issues with illness prognostication. It explains how well deep learning works for predicting diseases and demonstrates how deep learning and medicine will interact in the future. Medical research can benefit from deep learning's innovative feature extraction techniques.
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Mišić, Velibor V., Eilon Gabel, Ira Hofer, Kumar Rajaram, and Aman Mahajan. "Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission." Anesthesiology 132, no. 5 (May 1, 2020): 968–80. http://dx.doi.org/10.1097/aln.0000000000003140.
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Abstract Background Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of discharge. The authors hypothesized that machine learning approaches can be leveraged to accurately predict readmissions in postoperative patients from the emergency department. Further, the authors hypothesize that these approaches can accurately predict the risk of readmission much sooner than hospital discharge. Methods Using a cohort of surgical patients at a tertiary care academic medical center, surgical, demographic, lab, medication, care team, and current procedural terminology data were extracted from the electronic health record. The primary outcome was whether there existed a future hospital readmission originating from the emergency department within 30 days of surgery. Secondarily, the time interval from surgery to the prediction was analyzed at 0, 12, 24, 36, 48, and 60 h. Different machine learning models for predicting the primary outcome were evaluated with respect to the area under the receiver-operator characteristic curve metric using different permutations of the available features. Results Surgical hospital admissions (N = 34,532) from April 2013 to December 2016 were included in the analysis. Surgical and demographic features led to moderate discrimination for prediction after discharge (area under the curve: 0.74 to 0.76), whereas medication, consulting team, and current procedural terminology features did not improve the discrimination. Lab features improved discrimination, with gradient-boosted trees attaining the best performance (area under the curve: 0.866, SD 0.006). This performance was sustained during temporal validation with 2017 to 2018 data (area under the curve: 0.85 to 0.88). Lastly, the discrimination of the predictions calculated 36 h after surgery (area under the curve: 0.88 to 0.89) nearly matched those from time of discharge. Conclusions A machine learning approach to predicting postoperative readmission can produce hospital-specific models for accurately predicting 30-day readmissions via the emergency department. Moreover, these predictions can be confidently calculated at 36 h after surgery without consideration of discharge-level data. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New
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K., K., and Suribabu Korada. "Disease Prediction Using Machine Learning Approaches Considering Bio-Medical Signal Analysis: A Survey." Fusion: Practice and Applications 19, no. 2 (2025): 315–27. https://doi.org/10.54216/fpa.190223.
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In medical diagnosis and prognosis, symptoms provided by patients play a critical role in identifying diseases. Machine learning offers a powerful approach to analyzing and predicting illnesses based on these symptoms. In particular, classification algorithms are widely used to analyze input data and predict disease outcomes. A key factor in effective classification is the selection of relevant attributes, which directly affects the accuracy of the prediction. This research emphasizes the importance of proper feature extraction techniques in the context of disease prediction using biomedical signal analysis. Effective analysis requires both the extraction of critical features and the elimination of irrelevant data. The aim of this study is to explore existing approaches to disease prediction based on biomedical signal analysis. We focus on feature extraction from pre-processed data, which aids in distinguishing between different biomedical signals recorded by medical devices. Our objective is to identify biomedical cues that differentiate various health conditions. Examples of such signals include electroencephalogram (EEG), electrocardiogram (ECG), and electrogastrogram (EGG). Understanding how these signals differ between healthy and diseased states is crucial for accurate disease prediction. This research investigates diseases such as heart disease, kidney failure, and lung infections, considering how variations in biomedical signals can be used to predict the likelihood of severe illness. We continue to seek advancements in predicting and mitigating future health risks
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Snyder, Christopher, and Victor Brodsky. "Conformal Prediction and Large Language Models for Medical Coding." American Journal of Clinical Pathology 162, Supplement_1 (October 2024): S171—S172. http://dx.doi.org/10.1093/ajcp/aqae129.377.
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Abstract The assignment of current procedure terminology (CPT) codes to medical events is a highly cumbersome, logistic challenge for many healthcare organizations, as well as a significant contributor to medical expenses. Improvement in the allocation of medical resources and expenses dedicated to such tasks can be achieved through automation. However, because of the complex nature of medical records, automation of procedure terminologies is just now developing with the advent of machine learning methods. In this study, we develop a fine-tuned large language model (LLaMA-3B) as a high-reliability predictor for CPT codes. As input, we use 2018 pathology report text data, including gross report, microscopic description, brief medical history, and final diagnosis. We define our dataset with the top five most common technical component CPT codes, which account for 85% of all samples. As a (meta) predictor of the veracity of the large language model itself, we use the prediction’s softmax scalar value of the classification model, borrowing from similar recent approaches in conformal prediction. Specifically, the validation set, which is distinct from both the train and test set, is used to establish a prediction threshold below which the model withholds judgement. We show that, by fine tuning an off-the-shelf language model on pathology report text alone, we can achieve 95% prediction accuracy of the top 5 most common CPT codes on our dataset. We also then demonstrate the utility of combining large language models with conformal prediction. The two in combination raise our accuracy to 99.5% when we allow the model to abstain on making a prediction on 30% of the data, as is determined by a separate threshold on the scalar value of the predictor from the aforementioned validation set. We therefore present a highly flexible model for medical coding, which is simultaneously provably-reliable. Large language models can as such serve as a powerful tool for overcoming challenges in medical billing, thereby improving healthcare efficiency and reducing medical costs.
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Duraisamy, Balakrishnan, Rakesh Sunku, Krithik Selvaraj, Vishnu Vardhan Reddy Pilla, and Manoj Sanikala. "Heart disease prediction using support vector machine." Multidisciplinary Science Journal 6 (December 15, 2023): 2024ss0104. http://dx.doi.org/10.31893/multiscience.2024ss0104.
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Heart disease prediction through online consultation using machine learning refers to the application of advanced algorithms and techniques to analyze medical data collected during online consultations to predict the likelihood of an individual developing heart disease. Machine learning models are trained using historical data that includes various risk factors such as age, gender, blood pressure, cholesterol levels, and medical history. These models then utilize the input provided by patients during online consultations, such as symptoms, lifestyle habits, and additional medical tests, to generate personalized predictions about the probability of heart disease occurrence. By leveraging the power of machine learning, this approach aims to assist healthcare professionals in making more accurate diagnoses and providing timely recommendations for preventive measures or further medical intervention, ultimately improving patient outcomes and reducing the burden on healthcare systems. In this paper, a machine learning technique called Support Vector Machine (SVM) is used for heart disease prediction. Heart disease prediction through online consultation using SVM involves utilizing SVM as a machine learning algorithm to predict the likelihood of an individual having heart disease based on their consultation information provided online.
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Joshi, Deepika, Renu Kant, and Sachin Shakya. "The Disease prediction system using Machine learning." International Journal of Engineering and Computer Science 9, no. 2 (February 7, 2020): 24948–52. http://dx.doi.org/10.18535/ijecs/v9i2.4435.
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As, rise in the field of technology machine learning is widely used in various fields. Now it has various applications on the field of health industry. It works as a helping hand for the field of health industry. By the help of various machine learning algorithms, we can make various models for predicting the results through the large amount of dataset present in medical field. This paper comprises of efficient machine learning algorithms used in predicting disease through symptoms. As, the health industry has a huge amount of data for various fields so, we want to make a system where we can use various other applications of machine learning on health industry. This all had been done to make the better medical decisions and also for rise in the accuracy. As accurate analysis of the early prediction of disease helps in the patient care and the society services. These all challenges can be easier by the help of various tools, algorithms and framework provided by the machine learning. In addition to all these predictions we are making a chatbot for all that where patients can add the symptoms that are helpful to predict the disease and also check their diabetes status through the various information provided to system by the patients.
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Agada, Agada Vincent. "Predicting the Pharmaceutical Needs of Hospitals." nternational Journal of Public Health Pharmacy and Pharmacology 9, no. 1 (January 15, 2024): 1–13. http://dx.doi.org/10.37745/ijphpp.2013/vol9n1113.
Повний текст джерелаАнотація:
People’s lives are always threatened by various diseases. The role of health and medical services, in particular medicine, is undeniable in protecting their lives. Timely preparation and providing medicine for patients is vital since medicine shortage can endanger their lives while excessive accumulation of medicine can put them at expiration risk and waste health budgets. In this paper, we introduce a model for the prediction of commonly used medicine (type and amount) in hospitals. We have used a dataset of Govt. Hospital in Jos collected for three years consisting of 283 features, which included over 12293431 medicine and 9531 patients. Nine features were selected using experts’ feedback and were fed into the random forest and neural network algorithms. For the prediction task, medicine types and their amounts were predicted for everyone using different training sets. In addition, the right prediction time was also found, which is when predictions have promising accuracy while the executive team of a hospital has enough time to provide the right amounts of the most used medicine. The performance of algorithms was evaluated using a confusion matrix. Our results showed that the random forest had a promising performance in predicting the amounts of the most used medicine for a month using two years of data (accuracy 83.3%) while its accuracy in predicting medicine was 35.9%. Although our results are promising for predicting the amounts of medicine, these results could be enhanced and more reliable using other metadata like” patient underlying disease” and” medical tests result”.
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Avramovic, Aleksej, and Slavica Savic. "Lossless predictive compression of medical images." Serbian Journal of Electrical Engineering 8, no. 1 (2011): 27–36. http://dx.doi.org/10.2298/sjee1101027a.
Повний текст джерелаАнотація:
Among the many categories of images that require lossless compression, medical images can be indicated as one of the most important category. Medical image compression with loss impairs of diagnostic value, therefore, there are often legal restrictions on the image compression with losses. Among the common approaches to medical image compression we can distinguish the transformation-based and prediction-based approaches. This paper presents algorithms for the prediction based on the edge detection and estimation of local gradient. Also, a novel prediction algorithm based on advantages of standardized median predictor and gradient predictor is presented and analyzed. Removed redundancy estimation was done by comparing entropies of the medical image after prediction.
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Vllamasi, Andia, and Klejda Hallaçi. "Revolutionizing Healthcare: Disease Prediction Through Machine Learning Algorithms." Venturing into the Age of AI: Insights and Perspectives, no. 27 (October 1, 2023): 62–69. http://dx.doi.org/10.37199/f40002709.
Повний текст джерелаАнотація:
A crucial field of medical research is disease prediction, which has the potential to improve early diagnosis and therapy that can have a major impact on the course of treatment. By dramatically raising the standard of patient care and the effectiveness of the healthcare system as a whole, disease prediction plays a critical role in contemporary healthcare. Early detection of illnesses or medical issues, even before symptoms appear, is a key component of this proactive approach to healthcare management. This enables prompt interventions, better treatment outcomes, and better resource allocation. In this study, we use four different machine learning techniques to predict diseases using large datasets. Our main goal is to evaluate the effectiveness of different algorithms and determine which one performs best at accurately predicting the condition. To guarantee data quality and significance, the study makes considerable use of feature selection, engineering, and data preparation. Across various illness datasets, four machine learning algorithms, K-Nearest Neighbors, XG Boost, Ada Boost with SVM and Logistic Regression, are thoroughly examined. Accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve (AUC-ROC) are just a few of the performance criteria used to rate these algorithms. The comparative study not only identifies the algorithm with the best predicted accuracy, but it also offers insightful information about the benefits and drawbacks of each strategy. This study has significant healthcare impacts. We provide medical professionals with an effective tool for early detection and intervention by determining the algorithm that performs best at disease prediction. Improved disease prediction accuracy can result in earlier and more efficient treatment, which may save lives and lower healthcare costs. Additionally, this research opens the door for the application of sophisticated machine learning methods to clinical practice, ushering in a new era in healthcare where data-driven predictions support clinical judgment. In conclusion, by utilizing the potential of machine learning algorithms for more precise and timely disease prediction, our research supports the continual evolution of healthcare.