A Study of Heart Disease Diagnosis Using Machine Learning and Data Mining
Journal: Journal of Clinical Medicine Research DOI: 10.32629/jcmr.v5i4.3135
Abstract
This study explores the application of machine learning and data mining techniques for the diagnosis of heart disease. We focus on the development and evaluation of various machine learning models, including logistic regression, decision trees, random forests, support vector machines, and neural networks. These models are trained and tested on a comprehensive dataset, with performance assessed using accuracy, sensitivity, specificity, and the area under the ROC curve. Additionally, data mining techniques such as association rule mining and cluster analysis are employed to uncover underlying patterns and relationships within the data. The integration of these methods provides a multifaceted approach to diagnosing heart disease, offering insights into the heterogeneity of the condition and revealing subtypes with distinct characteristics. The study concludes that machine learning and data mining techniques have significant potential to enhance diagnostic accuracy and inform personalized treatment strategies in the field of cardiology.
Keywords
machine learning; data mining; heart disease diagnosis; predictive modeling
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[3]Ullah, Inam,Inayat, Tariq,Ullah, Naeem, et al.CLINICAL DECISION SUPPORT SYSTEM (CDSS) FOR HEART DISEASE DIAGNOSIS AND PREDICTION BY MACHINE LEARNING ALGORITHMS: A SYSTEMATIC LITERATURE REVIEW[J].JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY,2023,23(10).DOI:10.1142/S0219519423300016.
[4]Ahmed Telmoud, Cheikh Abdelkader,Saleck, Moustapha Mohamed,Tourad, Mohamedou Cheikh.ADVANCING HEART DISEASE DIAGNOSIS AND ECG CLASSIFICATION USING MACHINE LEARNING[J].Journal of Theoretical and Applied Information Technology,2024,102(06):2608-2623.
[5]Sheta, Alaa,ElAshmawi, Walaa,Baareh, Abdelkarim.Heart Disease Diagnosis Using Decision Trees with Feature Selection Method[J].INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY,2024,21(03):427-438.DOI:10.34028/iajit/21/3/7.
[6]Chih, WanLing,Tung, YuHsuan,Lussier, Eric C., et al.Associated factors with parental pregnancy decision-making and use of consultation after a prenatal congenital heart disease diagnosis[J].PEDIATRICS AND NEONATOLOGY,2023,64(04):371-380.DOI:10.1016/j.pedneo.2022.07.015.
[7]Watkins, S.,Isichei, O.,Gentles, T. L., et al.What is Known About Critical Congenital Heart Disease Diagnosis and Management Experiences from the Perspectives of Family and Healthcare Providers? A Systematic Integrative Literature Review[J].PEDIATRIC CARDIOLOGY,2023,44(02):280-296.DOI:10.1007/s00246-022-03006-8.
[8]Vasantrao, Bhandare Trupti,Rangasamy, Selvarani,Shelke, Chetan J.A Deep Learning Classification Approach using Feature Fusion Model for Heart Disease Diagnosis[J].INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS,2022,13(06):646-654.
[9]Kaixuan Wang,Cong Ye,Lan Luo, et al.Advances in radiation-induced heart disease diagnosis and treatment[J].Radiation Medicine and Protection,2024,5(02):83-89.DOI:10.1016/j.radmp.2024.04.003.
[10]Aliyar Vellameeran, Fathima,Brindha, Thomas.A new variant of deep belief network assisted with optimal feature selection for heart disease diagnosis using IoT wearable medical devices[J].COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING,2022,25(04):387-411.DOI:10.1080/10255842.2021.1955360.
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