Development of a Model for Predicting Treatment of Cardiovascular Diseases Based on Machine Learning Methods

DOI

10.2991/aebmr.k.200502.162How to use a DOI?

Keywords

logistic regression, decision trees, random forest, heart disease, learning algorithm

Abstract

This study aims to build a model for predicting cardiovascular disease in patients based on the analysis of personalized patient data cards. The forecast for the treatment of the heart disease clinic was determined using the method of logistic regression, random trees for the algorithm for constructing ID3 decision trees and the ensemble training method - random forest. As part of an experimental study, the effectiveness of the application of the considered methods for forecasting was evaluated based on the analysis of the ROC curve and the AUC metric. Experiments on real datasets of patient visits to the clinic showed that for short-term forecasting, the ID3 algorithm for constructing decision trees showed better results, and with an increase in the period under consideration, the method of logistic regression turned out to be more effective.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - I.P. Bolodurina
AU  - D.I. Parfenov
AU  - A.Yu. Zhigalov
AU  - L.S. Zabrodina
PY  - 2020
DA  - 2020/05/05
TI  - Development of a Model for Predicting Treatment of Cardiovascular Diseases Based on Machine Learning Methods
BT  - Proceedings of the 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth” (MTDE 2020)
PB  - Atlantis Press
SP  - 984
EP  - 989
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200502.162
DO  - 10.2991/aebmr.k.200502.162
ID  - Bolodurina2020
ER  -