Comparative Analysis of Machine Learning Methods for Prediction of Heart Diseases
Stepanyan I.V. Alimbayev C.A. Savkin M.O. Lyu D. Zidun M.
December 2022Pleiades Publishing
Journal of Machinery Manufacture and Reliability
2022#51Issue 8789 - 799 pp.
Abstract: The problem of combating cardiovascular diseases is becoming increasingly important due to the high level of disability and mortality from heart disease. In this paper, a study of methods for predicting heart disease using electrocardiography and machine learning algorithms was conducted. In total, during the study, 75 000 numerical experiments with various machine learning algorithms and their parameters were conducted. Based on the comparative analysis, the models and methods of machine learning were selected that gave the best results. The following methods were applied: logistic regression, k-nearest neighbors algorithm, decision tree, support-vector machine, Bayesian classifier, random forest, and deep neural networks. The selected models were generalized to identify their parameters and effective application.
correlation analysis , deep neural networks , digital signal processing , ECG , heart attack prediction , machine learning
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Mechanical Engineering Research Institute of the Russian Academy of Sciences, Moscow, Russian Federation
U. Joldasbekov Institute of Mechanics and Engineering, Almaty, Kazakhstan
Peoples’ Friendship University of Russia, Moscow, Russian Federation
Mechanical Engineering Research Institute of the Russian Academy of Sciences
U. Joldasbekov Institute of Mechanics and Engineering
Peoples’ Friendship University of Russia
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