Research on Predictive Models for Heart Disease Based on Machine Learning

DOI

10.2991/978-2-38476-585-0_19How to use a DOI?

Keywords

Heart disease prediction; Logistic regression; KNN; SVM; Random forests

Abstract

The heart is the core of the human circulatory system, providing a continuous supply of oxygenated blood to different organs in the body. However, when the heart can no longer effectively pump blood to the other organs, heart disease may develop. Therefore, the effective treatment and early diagnosis of heart disease have become the most significant issue in the medical field. Fortunately, the development of artificial intelligence and big data give birth to machine learning, and some statistical models in machine learning are applied to disease treatment and research. By applying machine learning to heart disease prediction, it can get a more accurate prediction with less time, thereby enhancing the reliability of predictive results. This thesis focuses on comparing diagnostic classifiers using five models in machine learning: logistic regression, k-nearest neighbor classification (KNN), decision tree, support vector machine (SVM), and random forest. The performance of the five models is then analyzed using various indicators, including accuracy, precision, recall, and F1 score. By applying different algorithms in machine learning to heart disease prediction, this research aims to achieve a more precise prediction and timely intervention before the outburst of heart disease, helping to reduce the incidence of heart disease.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Zhengkang Li
PY  - 2026
DA  - 2026/06/18
TI  - Research on Predictive Models for Heart Disease Based on Machine Learning
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 161
EP  - 166
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_19
DO  - 10.2991/978-2-38476-585-0_19
ID  - Li2026
ER  -