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Добірка наукової літератури з теми "HEART DISEASE PREDICTION"

Автор: Grafiati

Опубліковано: 19 січня 2024

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Статті в журналах з теми "HEART DISEASE PREDICTION"

Muthulakshmi, P., M. Parveen, and P. Rajeswari. "Prediction of Heart Disease using Ensemble Learning." Indian Journal Of Science And Technology 16, no. 20 (May 27, 2023): 1469–76. http://dx.doi.org/10.17485/ijst/v16i20.2279.

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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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Rani, K. Sandhya, M. Sai Manoj, and G. Suguna Mani. "A Heart Disease Prediction Model using Logistic Regression." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1463–66. http://dx.doi.org/10.31142/ijtsrd11401.

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Vinothini, S., Ishaan Singh, Sujaya Pradhan, and Vipul Sharma. "Heart Disease Prediction." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 750. http://dx.doi.org/10.14419/ijet.v7i3.12.16494.

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Machine learning algorithm are used to produce new pattern from compound data set. To cluster the patient heart condition to check whether his /her heart normal or stressed or highly stressed k-means clustering algorithm is applied on the patient dataset. From the results of clustering ,it is hard to elucidate and to obtain the required conclusion from these clusters. Hence another algorithm, the decision tree, is used for the exposition of the clusters of . In this work, integration of decision tree with the help of k-means algorithm is aimed. Another learning technique such as SVM and Logistics regression is used. Heart disease prediction results from SVM and Logistics regression were compared.

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Kumar, Prof K. Senthil. "HEART DISEASE PREDICTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (December 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27570.

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Heart disease is a major cause of death worldwide, making early diagnosis and prevention essential. Predictive models have gained significant attention in recent years, with several algorithms being employed to develop these models. However, there are challenges in implementing heart disease prediction models, including data quality, model accuracy, ethical concerns, and limited data. Therefore, this project aims to develop a heart disease prediction model and analyse different algorithms used in disease prediction. In order to increase the predictive accuracy of machine learning algorithms, this study compares six algorithms, including KNN (K-Nearest Neighbour), Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and Neural Network. 13 attributes, including age, sex, and cholesterol, are used, and ensemble methods like boosting and bagging are used. The accuracy, recall, f1 score, and precision of each algorithm are calculated to determine the most accurate model. Additionally, this study identifies the limitations of heart disease prediction models and their implications for patient diagnosis and treatment, by developing and analysing heart disease prediction models. In conclusion, while heart disease prediction models have the potential to be financially feasible and be useful in the future, their current limitations and challenges mean that they cannot be relied upon as the sole means of diagnosis or treatment decisions Key Words: Heart Diseases, Machine Learning Algorithms, Logistic Regression, Random Forest, Decision Tree.

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Bavani, B., S. Nirmala Sugirtha Rajini, M. S. Josephine, and V. Prasannakumari. "Heart Disease Prediction System based on Decision Tree Classifier." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1232–37. http://dx.doi.org/10.5373/jardcs/v11sp10/20192968.

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Ahmad, Bamanga Mahmud, Ahmadu Asabe Sandra, Musa Yusuf Malgwi, and Dahiru I. Sajoh. "Ensemble model for Heart Disease Prediction." Science Progress and Research 1, no. 4 (October 5, 2021): 268–80. http://dx.doi.org/10.52152/spr/2021.145.

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For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. Heart is one of the essential parts of human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical devices to support the systems and further to enhance the ability to diagnose and predict heart diseases. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researchers assessed that how to use ensemble model, which proposes a more stable performance than the use of base learning algorithm and these leads to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher developed the meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, as per the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has the high prediction probability score in the implementation of heart disease prediction.

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Khan, Sundas Naqeeb, Nazri Mohd Nawi, Asim Shahzad, Arif Ullah, Muhammad Faheem Mushtaq, Jamaluddin Mir, and Muhammad Aamir. "Comparative Analysis for Heart Disease Prediction." JOIV : International Journal on Informatics Visualization 1, no. 4-2 (November 15, 2017): 227. http://dx.doi.org/10.30630/joiv.1.4-2.66.

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Today, heart diseases have become one of the leading causes of deaths in nationwide. The best prevention for this disease is to have an early system that can predict the early symptoms which can save more life. Recently research in data mining had gained a lot of attention and had been used in different kind of applications including in medical. The use of data mining techniques can help researchers in predicting the probability of getting heart diseases among susceptible patients. Among prior studies, several researchers articulated their efforts for finding a best possible technique for heart disease prediction model. This study aims to draw a comparison among different algorithms used to predict heart diseases. The results of this paper will helps towards developing an understanding of the recent methodologies used for heart disease prediction models. This paper presents analysis results of significant data mining techniques that can be used in developing highly accurate and efficient prediction model which will help doctors in reducing the number of deaths cause by heart disease.

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Pati, Abhilash, Manoranjan Parhi, and Binod Kumar Pattanayak. "IHDPM: an integrated heart disease prediction model for heart disease prediction." International Journal of Medical Engineering and Informatics 14, no. 6 (2022): 1. http://dx.doi.org/10.1504/ijmei.2022.10044903.

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Дисертації з теми "HEART DISEASE PREDICTION"

Bolton, Jennifer Lynn. "Candidate genotypes in prediction of coronary heart disease." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/15877.

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Introduction There has been much discussion on personalised medicine; however use of genotype in risk prediction for coronary heart disease (CHD) has not resulted in appreciable improvements over non-genetic risk factors. The primary aim was to determine whether candidate single nucleotide polymorphisms (SNPs) identified from genome-wide association studies improved prediction of CHD over conventional risk factors (CRF). The secondary aim was to determine whether the use of apolipoproteins or lipoprotein(a) improved risk prediction of CHD. Methods Analyses used the Edinburgh Heart Disease Prevention Study (EHDPS), with 1592 men aged 30-59 and follow-up after 20 years; and the Edinburgh Artery Study (EAS), with 1592 men and women aged 54-75 and 15 years of follow-up. Candidate SNPs were identified by systematic literature reviews. CHD status was evaluated as severe (myocardial infarction or coronary revascularisation), and any (severe CHD, angina or non-specified ischaemic heart disease). Cox proportional hazards models were used to evaluate addition of candidate SNPs or lipids to models containing CRF. Results A group of genome-wide significant SNPs resulted in a non-significant improvement in C-index for severe CHD (0.038, p=0.082), and a significant improvement in C-index for any CHD (0.042, p=0.016); the associated net reclassification improvements (NRI) were 20.5% and 18.7%, respectively. Regression trees identified SNPs that were predictive of the remaining variance after adjusting for CRF; this resulted in a significant improvement in C-index for any CHD (0.031, p=0.008). The NRI were 11.0% and 9.6% for severe and any CHD, respectively. When compared with HDL cholesterol/total cholesterol, apolipoprotein AI/total cholesterol yielded a NRI of 3.3% for severe CHD. Lipoprotein(a) improved prediction of severe CHD, with a non-significant improvement in C-index (0.020, p=0.087), and NRI of 11.8%. Conclusion The results of this study indicate that a well selected group of candidate SNPs can improve risk prediction for CHD over-and-above CRF. The inclusion of lipoprotein(a), along with CRF, appeared to improve prediction of severe CHD, but not any CHD.

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Net, J. B. van der. "Towards genetic prediction of coronary heart disease in familial hypercholesterolemia." [S.l.] : Rotterdam : [The Author] ; Erasmus University [Host], 2009. http://hdl.handle.net/1765/14566.

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Van, Zyl Johet Engela. "Accuracy of risk prediction tools for acute coronary syndrome : a systematic review." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/97069.

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Thesis (MCur)--Stellenbosch University, 2015.
ENGLISH ABSTRACT: Background: Coronary artery disease is a form of cardiovascular disease (CVD) which manifests itself in three ways: angina pectoris, acute coronary syndrome and cardiac death. Thirty-three people die daily of a myocardial infarction (cardiac death) and 7.5 million deaths annually are caused by CVD (51% from strokes and 45% from coronary artery disease) worldwide. Globally, the CVD death rate is a mere 4% compared to South Africa which has a 42% death rate. It is predicted that by the year 2030 there will be 25 million deaths annually from CVD, mainly in the form of strokes and heart disease. The WHO compared the death rates of high-income countries to those of low- and middle-income countries, like South Africa, and the results show that CVD deaths are declining in high-income countries but rapidly increasing in low- and middle-income countries. Although there are several risk prediction tools in use worldwide, to predict ischemic risk, South Africa does not use any of these tools. Current practice in South Africa to diagnose acute coronary syndrome is the use of a physical examination, ECG changes and positive serum cardiac maker levels. Internationally the same practice is used to diagnose acute coronary syndrome but risk assessment tools are used additionally to this practise because of limitations of the ECG and serum cardiac markers when it comes to NSTE-ACS. Objective: The aim of this study was to systematically appraise evidence on the accuracy of acute coronary syndrome risk prediction tools in adults. Methods: An extensive literature search of studies published in English was undertaken. Electronic databases searched were Cochrane Library, MEDLINE, Embase and CINAHL. Other sources were also searched, and cross-sectional studies, cohort studies and randomised controlled trials were reviewed. All articles were screened for methodological quality by two reviewers independently with the QUADAS-2 tool which is a standardised instrument. Data was extracted using an adapted Cochrane data extraction tool. Data was entered in Review Manager 5.2 software for analysis. Sensitivity and specificity was calculated for each risk score and an SROC curve was created. This curve was used to evaluate and compare the prediction accuracy of each test. Results: A total of five studies met the inclusion criteria of this review. Two HEART studies and three GRACE studies were included. In all, 9 092 patients participated in the selected studies. Estimates of sensitivity for the HEART risks score (two studies, 3268 participants) were 0,51 (95% CI 0,46 to 0,56) and 0,68 (95% CI 0,60 to 0,75); specificity for the HEART risks score was 0,90 (95% CI 0,88 to 0,91) and 0,92 (95% CI 0,90 to 0,94). Estimates of sensitivity for the GRACE risk score (three studies, 5824 participants) were 0,03 (95% CI0,01 to 0,05); 0,20 (95% CI 0,14 to 0,29) and 0,79 (95% CI 0,58 to 0,93). The specificity was 1,00 (95% CI 0,99 to 1,00); 0,97 (95% CI 0,95 to 0,98) and 0,78 (95% CI 0,73 to 0,82). On the SROC curve analysis, there was a trend for the GRACE risk score to perform better than the HEART risk score in predicting acute coronary syndrome in adults. Conclusion: Both risk scores showed that they had value in accurately predicting the presence of acute coronary syndrome in adults. The GRACE showed a positive trend towards better prediction ability than the HEART risk score.
AFRIKAANSE OPSOMMING: Agtergrond: Koronêre bloedvatsiekte is ‘n vorm van kardiovaskulêre siekte. Koronêre hartsiekte manifesteer in drie maniere: angina pectoris, akute koronêre sindroom en hartdood. Drie-en-dertig mense sterf daagliks aan ‘n miokardiale infarksie (hartdood). Daar is 7,5 miljoen sterftes jaarliks as gevolg van kardiovaskulêre siektes (51% deur beroertes en 45% as gevolg van koronêre hartsiektes) wêreldwyd. Globaal is die sterfte syfer as gevolg van koronêre vaskulêre siekte net 4% in vergelyking met Suid Afrika, wat ‘n 42% sterfte syfer het. Dit word voorspel dat teen die jaar 2030 daar 25 miljoen sterfgevalle jaarliks sal wees, meestal toegeskryf aan kardiovaskulêre siektes. Die hoof oorsaak van sterfgevalle sal toegeskryf word aan beroertes en hart siektes. Die WHO het die sterf gevalle van hoeinkoms lande vergelyk met die van lae- en middel-inkoms lande, soos Suid Afrika, en die resultate het bewys dat sterf gevalle as gevolg van kardiovaskulêre siekte is besig om te daal in hoe-inkoms lande maar dit is besig om skerp te styg in lae- en middel-inkoms lande. Daar is verskeie risiko-voorspelling instrumente wat wêreldwyd gebruik word om isgemiese risiko te voorspel, maar Suid Afrika gebruik geen van die risiko-voorspelling instrumente nie. Huidiglik word akute koronêre sindroom gediagnoseer met die gebruik van n fisiese ondersoek, EKG verandering en positiewe serum kardiale merkers. Internationaal word die selfde gebruik maar risiko-voorspelling instrumente word aditioneel by gebruik omdat daar limitasies is met EKG en serum kardiale merkers as dit by NSTE-ACS kom. Doelwit: Die doel van hierdie sisematiese literatuuroorsig was om stelselmatig die bewyse te evalueer oor die akkuraatheid van akute koronêre sindroom risiko-voorspelling instrumente vir volwassenes. Metodes: 'n Uitgebreide literatuursoektog van studies wat in Engels gepubliseer is was onderneem. Cochrane biblioteek, MEDLINE, Embase en CINAHL databases was deursoek. Ander bronne is ook deursoek. Die tiepe studies ingesluit was deurnsee-studies, kohortstudies en verewekansigde gekontroleerde studies. Alle artikels is onafhanklik vir die metodologiese kwaliteit gekeur deur twee beoordeelaars met die gebruik van die QUADAS-2 instrument, ‘n gestandaardiseerde instrument. ‘n Aangepaste Cochrane data instrument is gebruik om data te onttrek. Data is opgeneem in Review Manager 5.2 sagteware vir ontleding. Sensitiwiteit en spesifisiteit is bereken vir elke risiko instrument en ‘n SROC kurwe is geskep. Die SROC kurwe is gebruik om die akkuraatheid van voorspelling van elke instrument te evalueer en te toets. Resultate: Twee HEART studies en drie GRACE studies is ingesluit. In total was daar 9 092 patiente wat deelgeneeem het in die gekose studies. Skattings van sensitiwiteit vir die HEART risiko instrument (twee studies, 3268 deelnemers) was 0,51 (95% CI 0,47 to 0,56) en 0,68 (95% CI 0,60 to 0,75) spesifisiteit vir die HEART risiko instrument was 0,89 (95% CI 0,88 to 0,91) en 0,92 (95% CI 0,90 to 0,94). Skattings van sensitiwiteit vir die GRACE risiko instrument (drie studies, 5824 deelnemers) was 0,28 (95% CI 0,13 to 0,53); 0,20 (95% CI 0,14 to 0,29) en 0,79 (95% CI 0,58 to 0,93). Die spesifisiteit vir die GRACE risiko instrument was 0,97 (95% CI 0,95 to 0,99); 0,97 (95% CI 0,95 to 0,98) en 0,78 (95% CI 0,73 to 0,82). Met die SROC kurwe ontleding was daar ‘n tendens vir die GRACE risiko instrument om beter te vaar as die HEART risiko instrument in die voorspelling van akute koronêre sindroom in volwassenes. Gevolgtrekking: Altwee risiko instrumente toon aan dat albei instrumente van waarde is. Albei het die vermoë om die teenwoordigheid van akute koronêre sindroom in volwassenes te voorspel. Die GRACE toon ‘n positiewe tendens teenoor beter voorspelling vermoë as die HEART risiko instrument.

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Savatteri, Giuseppe. "Enviromental factors influencing heart diseases." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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È importante individuare strategie e meccanismi per sfruttare le opportunità offerte dalla digitalizzazione in ambito sanitario delle attività e dalla gestione dei Big Data per migliorare efficienza, monitoraggio, prevenzione e cura delle patologie. possibilità di applicazione della medicina di precisione, refertazione in anatomia patologica e automazione della raccolta dati. Il lavoro di tesi è incentrato sulle problematiche legate alle malattie cardio-cerebrovascolari e la loro correlazione con il territorio con cui i soggetti interagiscono. È stato realizzato un nuovo sistema di monitoraggio del territorio che possa individuare possibili legami tra la composizione del territorio, in termini di opere pubbliche e private, e lo stato di salute delle persone. Un apposito insieme di dati viene fornito al modello di modello di machine learning, basato sulle reti neurali e provvederà ad operare nell'estrazione di possibili correlazioni tra la tipologia di edifici e la percentuale di soggetti a rischio cardiovascolare.

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Li, Jianeng. "Research on a Heart Disease Prediction Model Based on the Stacking Principle." Thesis, Högskolan Dalarna, Informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-34591.

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In this study, the prediction model based on the Stacking principle is called the Stacking fusion model. Little evidence demonstrates that the Stacking fusion model possesses better prediction performance in the field of heart disease diagnosis than other classification models. Since this model belongs to the family of ensemble learning models, which has a bad interpretability, it should be used with caution in medical diagnoses. The purpose of this study is to verify whether the Stacking fusion model has better prediction performance than stand-alone machine learning models and other ensemble classifiers in the field of heart disease diagnosis, and to find ways to explain this model. This study uses experiment and quantitative analysis to evaluate the prediction performance of eight models in terms of prediction ability, algorithmic stability, false negative rate and run-time. It is proved that the Stacking fusion model with Naive Bayes classifier, XGBoost and Random forest as the first-level learners is superior to other classifiers in prediction ability. The false negative rate of this model is also outstanding. Furthermore, the Stacking fusion model is explained from the working principle of the model and the SHAP framework. The SHAP framework explains this model’s judgement of the important factors that influence heart disease and the relationship between the value of these factors and the probability of disease. Overall, two research problems in this study help reveal the prediction performance and reliability of the cardiac disease prediction model based on the Stacking principle. This study provides practical and theoretical support for hospitals to use the Stacking principle in the diagnosis of heart disease.

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Rice, Thomas H. "Prediction of oxygen consumption during exercise testing in apparently healthy subjects and coronary artery disease patients." Thesis, Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/91143.

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The American College of Sports Medicine has published formulae that are widely used to predict functional aerobic capacity for any treadmill speed and grade combination. However, it has been demonstrated that these formulae overpredict oxygen consumption (V̇O₂) for patients with coronary artery disease as well as for apparently healthy individuals. To study this, we measured V̇O₂, ventilation (V̇_E), and respiratory exchange ratio (R) responses in 21 apparently healthy subjects (AH) and 16 coronary artery diseased subjects (CAD) during a modified Balke protocol. Modification of the protocol consisted of extending the stage time from two minutes to three minutes at the higher intensities to allow a greater time for a physiological steady-state to occur. The attainment of a steady-state may lead to the reduction of or the elimination of prediction errors. No differences were observed between two and three minute VO₂ responses at maximal and submaximal exercise for either group. At peak exercise, the AH group was significantly (P≤.05) different from the CAD group when compared for heart rate (164±2.6 vs 140±4.8 bts•min⁻¹ ), V̇O₂ (33.3±1.1 vs 26.7±2.3 ml•kg⁻¹•min⁻¹), and total treadmill time (9.9±.33 vs 8.1±.54 min). At submaximal exercise, V̇O₂ responses were also significantly (p≤.05) greater for the AH group when compared to the CAD group (26.6±.95 vs 21.9±1.8 ml•kg⁻¹•min⁻¹). No significant differences were observed for RPE and blood lactate at peak exercise and V̇_E and R responses at submaximal or peak exercise between the two groups. Predicted values for peak V̇O₂ were significantly (p≤.05) higher than measured values (33.3±1.1 vs 38.8±1.1 ml•kg⁻¹•min⁻¹) and (26.7±2.3 vs 34.1±1.7 ml•kg⁻¹•min⁻¹) for the AH and CAD groups, respectively. However, no significant differences were noted between predicted and measured V̇O₂ responses at submaximal exercise for either group. Individuals classified as Type A were not significantly different from classified Type B individuals when compared for the cardio-respiratory variables measured. These data demonstrate that the ACSM prediction formulae significantly overpredict V̇O₂ for both AH and CAD subjects at maximal treadmill intensities. However, at submaximal intensities, these prediction formulae are acceptable for both groups of subjects. Furthermore, these data suggest that two minutes per stage allows sufficient time for physiological steady-state to occur at clearly submaximal intensities. Although at the higher intensities, extending the stage time beyond two minutes may be indicated.
M.S.

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Vedin, Ola. "Prevalence and Prognostic Impact of Periodontal Disease and Conventional Risk Factors in Patients with Stable Coronary Heart Disease." Doctoral thesis, Uppsala universitet, Institutionen för medicinska vetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260564.

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The purpose of this thesis was to assess the prevalence and management of established cardiovascular (CV) risk factors and the prevalence and influence of self-reported markers (number of teeth and frequency of gum bleeding) of periodontal disease (PD), a less explored CV risk factor, in patients with stable chronic coronary heart disease (CHD). We studied patients from the global STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY (STABILITY) trial (n=15,828), in which patients with stable chronic CHD were randomized to either darapladib or placebo. Our studies were performed using descriptive statistics and multivariable linear, logistic and Cox regression models. The use of secondary preventive medications was generally high across the whole study population. Despite this, CV risk factors were highly prevalent, including obesity, hypertension and hypercholesterolemia. Achievement of guideline-recommended treatment targets was lacking and little improvement was seen throughout the study duration. Approximately 40% of patients reported having <15 remaining teeth and 25% reported gum bleeding. More tooth loss was associated with a greater CV risk factor burden after adjustment, while the associations for gum bleeding were less evident. After multivariable adjustment for CV risk factors and socioeconomic status, more tooth loss was associated with an increased risk of major adverse CV events (a composite of CV death, myocardial infarction and stroke), CV mortality, all-cause mortality and fatal or non-fatal stroke. We found associations between a higher degree of tooth loss and elevated levels of several prognostic biomarkers known to reflect various pathophysiological mechanisms involved in CV morbidity and mortality. Most biomarkers had little attenuating effect on the relationship between tooth loss and outcomes in a multivariable model. In conclusion, we found an inadequate CV risk factor control despite a high use of evidence-based pharmacological therapies, likely to explain some of the excess risk in CHD patients. Further, we demonstrated a high prevalence of PD markers, tooth loss in particular, that were associated with a wide range of established CV risk factors, prognostic biomarkers and outcomes. Collectively, these findings indicate that tooth loss may be a significant risk factor among patients with stable chronic CHD.

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Atsalakis, Mihalis. "Prediction of initial involvement of first grade Greek school children in an out-of-school, organized, community physical activity programme : an application of the theory of planned behaviour." Thesis, University of Hull, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262408.

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Zuo, Heng. "3D Multi-Physics MRI-Based Human Right Ventricle Models for Patients with repaired Tetralogy of Fallot: Cardiac Mechanical Analysis and Surgical Outcome Prediction." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/478.

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Introduction. Computational modelling has been used widely in biological and clinical applications, but relatively less in surgical design and optimization. Magnetic resonance image (MRI)-based right ventricle (RV) models were introduced for patients with repaired Tetralogy of Fallot (rTOF) to assess ventricle cardiac function, and to identify morphological and mechanical parameters which can be used to predict and optimize post-surgery cardiac outcome. Tetralogy of Fallot is a common congenital heart defect which includes a ventricular septal defect and severe right ventricular outflow obstruction, account for the majority of cases with late onset RV failure. The current surgical approach for the patients with repaired ToF including pulmonary valve replacement/insertion (PVR) has yielded mixed results. It is of great interest to identify parameters which may be used to predict surgical cardiac function outcome after PVR. Data, Model, and Methods. Cardiac Magnetic Resonance (CMR) data from 20 healthy volunteers (11 males, mean year : 22.8) and 56 TOF patients (37 males, mean year : 25.3) were provided by Children's Hospital - Boston, Harvard Medical School from our NIH-funded project (R01 HL089269). RV wall thickness (WT), circumferential and longitudinal curvature (C-cur and L-cur), surface area (SA) and surface to volume ratio (SVR) were obtained based on CMR data for morphological analysis. 6 healthy volunteers and 16 TOF patients were chosen to construct 3D computational models for mechanical analysis. The 3D CMR-based RV/LV/Patch combination models included a) isotropic and anisotropic material properties, b) myocardial fiber orientation, c) active contraction with two zero-load geometries, and d) fluid-structure interactions. The models were used to obtain the assessment for RV mechanical conditions, which might be helpful for PVR surgical outcome prediction. All the computational models were built and solved in a commercial finite element software ADINA. Statistical methods including Linear Mixed- effort Method and Logistical regression were used in the morphological and mechanical analysis to find out potential indicators for predicting PVR outcome from the morphological and mechanical parameters. Results. In morphological analysis, statistically significant differences were found in RV SA and SVR between better-outcome patient group (BPG) and worse-outcome patient group (WPG). At begin of ejection, mean RV SA of BPG was 13.6% lower than that from WPG (241.1 cm2 v.s. 279.0 cm2, p =0.0161). Mean RV SVR of BPG was 13.1% lower than that from WPG (1.26 cm2/ml v.s. 1.45 cm2/ml, p =0.0271). Similar results were also found in RV SA and SVR at begin of filling. Furthermore, RV EF change from pre- to post-PVR were found negatively correlated with RV SA and SVR. In mechanical analysis, 22 structure-only models with one zero-load geometry (1G) were constructed to obtain stress/strain distributions. Stress-P1 from BPG was found to be closer to that from HG, compared to Stress- P1 of WPG. At the beginning of ejection, mean Stress-P1 of BPG was only 6.8% higher than that from healthy group (p =0.6889), while average Stress-P1 of WPG was 84.1% higher than that of healthy group (p =0.0418). Similar results were also found at begin of filling. The results suggested that comparing patients' RV stress values with healthy RV stress values may help identify patients with possible better outcome. The models with two zero-load geometries (2G models) and FSI models were also constructed. Their numerical results indicated that 2G models can provide end-ejection and end-filling results which were not available in 1G models, and FSI models can provide flow velocity, pressure and shear stress information which lacked in structure-only models (1G and 2G models). Conclusion. In vivo image-based 3D patient- specific computational models could lead to considerable potential gain not only in surgical design and outcome prediction, but also in understanding the mechanisms of RV failure for patients with repaired TOF.

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Задойоний, Віктор Андрійович, та Viktor Zadoionyi. "Комп’ютерна система прогнозування ризиків появи серцевих захворювань". Bachelor's thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2021. http://elartu.tntu.edu.ua/handle/lib/35577.

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У кваліфікаційній роботі бакалавра спроектовано комп’ютерну систему прогнозування ризиків появи серцевих захворювань. До її складу входять дві основні компоненти: підсистема збору даних життєвих показників людини та інтелектуальний модуль прогнозування ризиків появи серцевих захворювань. Підсистему збору життєвих показників людини реалізовано за допомогою мобільних сенсорів, які кріпляться до організму людини, однокристального міні-комп’ютера Raspberry PI, як центрального вузла, що виконує функції контролера при передачі даних та хмарного сховища – для зберігання та аналізу зібраної інформації. В якості сенсорів, які кріпляться до тіла людини використано: пульсометр, давач температури тіла та вимірювач артеріального тиску. Окрім цього, для врахування параметрів навколишнього середовища, у якому перебуває людина, застосовано сенсори аналізу параметрів повітря: температури, вологості, наявності шкідливих речовин та інтенсивності світла. Інтелектуальний модуль прогнозування ризиків появи серцевих захворювань реалізовано за допомогою мови програмування Python та із застосування відкритих бібліотек машинного навчання. При моделюванні використано і досліджено характеристики шести моделей бінарної класифікації, у результаті якого встановлено, що найбільш ефективною є модель на основі випадкові лісів (Random Forest), що забезпечує точність прогнозування на рівні 97,5%.
The computer system for predicting the risk of heart disease has been designed in the bachelor's thesis. It consists of two main components: a subsystem for collecting data on human vital signs and an intelligent module for predicting the risk of heart disease. The human vital signs collection subsystem is implemented using mobile sensors attached to the human body, a single-chip Raspberry PI mini-computer, as a central node that acts as a controller for data transmission and cloud storage - to store and analyze the collected information. The following sensors attached to the human body: a heart rate monitor, a body temperature sensor and a blood pressure monitor. In addition, sensors for analyzing air parameters: temperature, humidity, the presence of harmful substances and light intensity were using to take into account the parameters of the environment in which a person is. The intelligent module for predicting the risk of heart disease is implemented using the Python programming language and the use of open machine learning libraries. The simulation used and investigated the characteristics of six models of binary classification, as a result of which it was found that the most effective model is based on random forests (Random Forest), which provides forecasting accuracy of 97.5%.
ПЕРЕЛІК ОСНОВНИХ УМОВНИХ ПОЗНАЧЕНЬ, СИМВОЛІВ І СКОРОЧЕНЬ 7 ВСТУП 8 РОЗДІЛ 1 АНАЛІЗ ВИМОГ ТА ОСОБЛИВОСТЕЙ ПРОЕКТУВАННЯ КОМП’ЮТЕРНИХ СИСТЕМ МЕДИЧНОГО ПРИЗНАЧЕННЯ 9 1.1 Аналіз технічного завдання на проектування комп’ютерної системи прогнозування ризиків появи серцевих захворювань 9 1.2 Обґрунтування доцільності та особливості проектування комп’ютерної системи 17 РОЗДІЛ 2 МОДЕЛЬ ТА СТРУКТУРА КОМП’ЮТЕРНОЇ СИСТЕМИ ПРОГНОЗУВАННЯ РИЗИКІВ ПОЯВИ СЕРЦЕВИХ ЗАХВОРЮВАНЬ 22 2.1 Моделі та архітектури комп’ютерних систем у сфері охорони здоров’я 22 2.2 Фактори впливу навколишнього середовища на стан організму людини та комп’ютерна система автоматизованого їх аналізу 25 2.3 Різновиди та розвиток IoT у медичній галузі 29 2.4 Проектування архітектури комп’ютерної системи прогнозування ризиків появи серцевих захворювань 34 РОЗДІЛ 3 ПРОГРАМНА МОДЕЛЬ ІНТЕЛЕКТУАЛЬНОГО МОДУЛЯ ПРОГНОЗУВАННЯ РИЗИКІВ ВИНИКНЕННЯ СЕРЦЕВИХ ЗАХВОРЮВАНЬ 42 3.1 Аналіз відкритих джерел даних для побудови моделі прогнозування щодо виникнення серцевих захворювань 42 3.2 Препроцесинг даних 44 3.3 Виявлення залежностей між ознаками вхідного набору даних 51 3.4 Виявлення значимих ознак набору даних 56 3.5 Реалізація моделей прогнозування розвитку серцевих захворювань 59 РОЗДІЛ 4 БЕЗПЕКА ЖИТТЄДІЯЛЬНОСТІ, ОСНОВИ ОХОРОНИ ПРАЦІ 67 ВИСНОВКИ 68 СПИСОК ВИКОРИСТАНИХ ДЖЕРЕЛ 69 Додаток A. Технічне завдання

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Книги з теми "HEART DISEASE PREDICTION"

A, De Lemos James, and American Heart Association, eds. Biomarkers in heart disease. Malden, Mass: Blackwell, 2008.

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A, De Lemos James, and American Heart Association, eds. Biomarkers in heart disease. Malden, Mass: Blackwell Pub., 2008.

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Accuracy of a treadmill scoring system for prediction of coronary artery disease in female subjects. 1991.

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Kornitzer, M., and R. Goldberg. Contribution of Long-term Follow-up to the Prediction of Coronary Heart Disease (Cardiology). S Karger Ag, 1993.

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Mills, Gary H. Pulmonary disease and anaesthesia. Edited by Philip M. Hopkins. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0082.

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Respiratory adverse events are the commonest complications after anaesthesia and have profound implications for the recovery of the patient and their subsequent health. Outcome prediction related to respiratory disease and complications is vital when determining the risk:benefit balance of surgery and providing informed consent. Surgery produces an inflammatory response and pain, which affects the respiratory system. Anaesthesia produces atelectasis, decreases the drive to breathe, and causes muscle weakness. As the respiratory system ages, closing capacity increases and airway closure becomes an increasing issue, resulting in atelectasis. Increasing comorbidity and polypharmacy reduces the patient’s ability to eliminate drugs. The proportion of major operations on older frailer patients is rising and postoperative recovery becomes more complicated and the demand for critical care rises. At the same time, the population is becoming more obese, producing rapid decreases in end-expiratory lung volume on induction, together with a high incidence of sleep-disordered breathing. Despite this, many high-risk patients are not accurately identified preoperatively, and of those that are admitted to critical care, some are discharged and then readmitted to the intensive care unit with complications. Respiratory diseases may lead to increases in pulmonary vascular resistance and increased load on the right heart. Some lung diseases are primarily fibrotic or obstructive. Some are inflammatory, autoimmune, or vasculitic. Other diseases relate to the drive to breathe, the nerve supply to, or the respiratory muscles themselves. The range of types of respiratory disease is wide and the physiological consequences of respiratory support are complex. Research continues into the best modes of respiratory support in theatre and in the postoperative period and how best to protect the normal lung. It is therefore essential to understand the effects of surgery and anaesthesia and how this impacts existing respiratory disease, and the way this affects the balance between load on the respiratory system and its capacity to cope.

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Masuda, Atsuro, Masanao Naya, Keiichiro Yoshinaga, and Nagara Tamaki. Imaging of Myocardial Innervation. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0023.

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Cardiac autonomic innervation imaging has been focused on assessing molecular, electrophysiologic, and pathophysiologic processes of various cardiac disorders. Iodine radiotracer (I-123)-labeled metaiodobenzylguanidine (MIBG), as a marker of adrenergic neuron function, plays an important role in risk stratification and treatment monitoring of heart failure patients. In addition, MIBG has a potential value for predicting fatal arrhythmias that may require implantable cardioverter-defibrillator treatment. Among various positron emission tomography (PET) tracers for probing autonomic neuronal function, C-11 hydroxyephedrine (HED), has been used for precise assessment of heart failure and arrhythmias, similar to MIBG. More studies are needed to confirm the clinical utility of these molecular imaging modalities for the management of patients with heart failure, coronary artery disease and arrhythmias.

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Cardiac patients' attitude toward adherence to an exercise medical regimen. 1988.

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Cardiac patients' attitude toward adherence to an exercise medical regimen. 1988.

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Cardiac patients' attitude toward adherence to an exercise medical regimen. 1985.

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Cardiac patients' attitude toward adherence to an exercise medical regimen. 1988.

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Частини книг з теми "HEART DISEASE PREDICTION"

Dutta, Pijush, Shobhandeb Paul, Neha Shaw, Susmita Sen, and Madhurima Majumder. "Heart Disease Prediction." In Artificial Intelligence and Cybersecurity, 1–18. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003097518-1.

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Neto, Cristiana, Diana Ferreira, José Ramos, Sandro Cruz, Joaquim Oliveira, António Abelha, and José Machado. "Prediction Models for Coronary Heart Disease." In Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference, 119–28. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86261-9_12.

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Jha, Rahul Kumar, Santosh Kumar Henge, and Ashok Sharma. "Heart Disease Prediction and Hybrid GANN." In Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation, 438–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85577-2_52.

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Adhikari, Bikal, and Subarna Shakya. "Heart Disease Prediction Using Ensemble Model." In Lecture Notes in Networks and Systems, 857–68. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7657-4_69.

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Sai Shekhar, M., Y. Mani Chand, and L. Mary Gladence. "Heart Disease Prediction Using Machine Learning." In Advances in Systems, Control and Automations, 603–9. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8685-9_63.

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Patel, Jaydutt, Azhar Ali Khaked, Jitali Patel, and Jigna Patel. "Heart Disease Prediction Using Machine Learning." In Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, 653–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0733-2_46.

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Sharma, Sumit, Vishu Madaan, Prateek Agrawal, and Narendra Kumar Garg. "Heart Disease Prediction Using Fuzzy System." In Communications in Computer and Information Science, 424–34. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3140-4_38.

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Bhatia, Meenu, and Dilip Motwani. "Heart Disease Prediction Using Ensemblers Learning." In Information and Communication Technology for Intelligent Systems, 733–43. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7078-0_73.

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Patil, Saraswati, Pavan Kumar Sanjay, Harsh Pardeshi, Niraj Patil, Omkar Pawar, and Prishita Jhamtani. "Heart Disease Prediction Using Supervised Learning." In ICT for Intelligent Systems, 385–94. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3982-4_33.

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Siva Rama Krishna, Ch, M. Vasanthi, K. Hemanth Reddy, and G. Jaswanth. "Heart Disease Prediction Using Machine Learning." In Intelligent Manufacturing and Energy Sustainability, 589–95. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8497-6_53.

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Тези доповідей конференцій з теми "HEART DISEASE PREDICTION"

Javangula, Upagnaa, Teja Rani Banna, and Janardhana Rao Alapati. "Heart Disease Prediction." In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2023. http://dx.doi.org/10.1109/icccnt56998.2023.10306980.

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Veena, N., S. Mahalakshmi, G. Anisha Diyya, Alekhya Allada, and Malavika S. Anand. "Heart Disease Prediction System." In 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS). IEEE, 2021. http://dx.doi.org/10.1109/fabs52071.2021.9702552.

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Mahfuri, Mahmoud, Taher M. Ghazal, Muhammad Mudassar, Shahan Yamin Siddiqui, Sajid Farooq, Nayab Kanwal, and Munir Ahmad. "Medical Diagnoses: Heart Disease Prediction." In 2023 International Conference on Business Analytics for Technology and Security (ICBATS). IEEE, 2023. http://dx.doi.org/10.1109/icbats57792.2023.10111497.

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Doki, Srichand, Siddhartha Devella, Sumanth Tallam, Sai Sujeeth Reddy Gangannagari, P. Sampathkrishna Reddy, and G. Pradeep Reddy. "Heart Disease Prediction Using XGBoost." In 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917678.

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Rahman, Mafizur, Maryam Mehzabin Zahin, and Linta Islam. "Effective Prediction On Heart Disease: Anticipating Heart Disease Using Data Mining Techniques." In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019. http://dx.doi.org/10.1109/icssit46314.2019.8987776.

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Bilgaiyan, Saurabh, Tajul Islam Ayon, Aliza Ahmed Khan, Fatema Tuj Johora, Masuma Parvin, and Mohammad Jahangir Alam. "Heart disease Prediction Using Machine Learning." In 2023 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2023. http://dx.doi.org/10.1109/iccci56745.2023.10128378.

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Ghazal, Taher M., Amer Ibrahim, Ali Sheraz Akram, Zahid Hussain Qaisar, Sundus Munir, and Shanza Islam. "Heart Disease Prediction Using Machine Learning." In 2023 International Conference on Business Analytics for Technology and Security (ICBATS). IEEE, 2023. http://dx.doi.org/10.1109/icbats57792.2023.10111368.

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Song, Siyue, Tianhua Chen, and Grigoris Antoniou. "ANFIS Models for Heart Disease Prediction." In ICIAI 2021: 2021 the 5th International Conference on Innovation in Artificial Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3461353.3461354.

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Sanyal, Saptarsi, Dolly Das, Saroj Kumar Biswas, Manomita Chakraborty, and Biswajit Purkayastha. "Heart Disease Prediction Using Classification Models." In 2022 3rd International Conference for Emerging Technology (INCET). IEEE, 2022. http://dx.doi.org/10.1109/incet54531.2022.9824651.

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Srivastava, Asmit, and Ashish kumar Singh. "Heart Disease Prediction using Machine Learning." In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2022. http://dx.doi.org/10.1109/icacite53722.2022.9823584.

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Звіти організацій з теми "HEART DISEASE PREDICTION"

Kent, David M., Jason Nelson, Jenica N. Upshaw, Gaurav Gulati, Riley Brazil, Esmee Venema, Christine M. Lundquist, et al. Using Different Data Sets to Test How Well Clinical Prediction Models Work to Predict Patients' Risk of Heart Disease. Patient-Centered Outcomes Research Institute (PCORI), September 2021. http://dx.doi.org/10.25302/09.2021.me.160635555.

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Wei, Dongmei, Yang Sun, and Rongtao Chen. Risk prediction model for ISR after coronary stenting-a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2023. http://dx.doi.org/10.37766/inplasy2023.4.0014.

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Review question / Objective: The efficacy of risk prediction model for ISR. Condition being studied: Coronary heart disease (CHD), with high morbidity and high mortality rate, is still a serious public health concern around the world. PCI is fast becoming a key instrument in revascularization for patients with CHD, as well as an important technology in the management of CHD patients.1 Although the clinical application of coronary stents brought about a dramatic improvement in patients’ clinical and procedural outcomes, the mid-and long-term outcome of stent implantation remains significantly hampered by the risk of developing ISR with a prevalence rate of 3–20% over time. Predictive models have the advantage of formally combining risk factors to allow more accurate risk estimation. And it is essential to establish a model to predict ISR in patients with CAD and drug-eluting stents (DESs) implantation.However, predictive model performance needs further evaluation.

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Rahman, Kazi, Grace Lee, Kristina Vine, Amba-Rose Atkinson, Michael Tong, and Veronica Matthews. Impacts of climate change on health and health services in northern New South Wales: an Evidence Check rapid review. The Sax Institute, December 2022. http://dx.doi.org/10.57022/xlsj7564.

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This rapid review investigated the effects of climate change on health and health services in northern NSW—a known ‘hotspot’ for natural disasters—over the next 10-20 years. It included 92 peer-reviewed articles and 9 grey literature documents, with 17% focused on Northern NSW. Climate change will cause both an increase in average temperatures and in extreme weather events and natural disasters. Impacts particularly affecting Northern NSW are expected to include increases and exacerbations of: mental illness; infectious diseases, including those transmitted by mosquitoes, water and food; heat-related illnesses; chronic diseases including respiratory and cardiac conditions; injuries; and mortality—with vulnerable groups being most affected. Demand for health services will increase, but there will also be disruptions to medication supply and service availability. A whole-of-system approach will be needed to address these issues. There are numerous gaps in the research evidence and a lack of predictive modelling and robust locally relevant data.

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