Predicting Heart Disease Using Multiple Supervised Learning Methods

DOI

10.2991/978-94-6463-540-9_23How to use a DOI?

Keywords

Heart Disease; Supervised Learning; Binary Classification; Model Performance Evaluation

Abstract

Cardiovascular diseases (CVDs) are responsible for a significant number of deaths worldwide and are one of the leading causes of mortality. The main types of CVDs include coronary heart disease, rheumatic heart disease, and congenital heart disease. This article employs logistic regression to investigate whether individual features can predict heart disease. Following model training, the accuracy of each individual feature’s prediction results is documented. The analysis reveals that most features alone cannot effectively predict heart disease. However, clinical features such as chest pain type, ST slope, and oldpeak (measured by ST depression) demonstrate relatively high accuracy. To explore how the combination of all features can predict heart disease, decision tree, random forest, XGBoost, and neural network models are utilized in this study. After model training, their performance is assessed and compared using various evaluation metrics including precision, recall, confusion matrices displayed in heat maps, ROC curves, and loss curves. The results indicate that the random forest model outperforms the others across all evaluation metrics, establishing it as the most effective model developed in this study for predicting heart disease.

Copyright

© 2024 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.

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TY  - CONF
AU  - Hangcen Xie
PY  - 2024
DA  - 2024/10/16
TI  - Predicting Heart Disease Using Multiple Supervised Learning Methods
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 203
EP  - 215
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_23
DO  - 10.2991/978-94-6463-540-9_23
ID  - Xie2024
ER  -