Heart Disease Prediction Using Machine Learning Models

DOI

10.2991/978-94-6239-648-7_69How to use a DOI?

Keywords

Heart Disease; Machine Learning; Random Forest; Classification; Medical Diagnosis

Abstract

This study is aimed at the binary classification task of heart diseases, using 918 subjects and 11 clinical and diagnostic features from public datasets. Before training, the data underwent missing and anomaly checks, numerical features were standardized, category-specific features were encoded, and stratified sampling was used to divide the training/test sets in an 8:2 ratio. Some models enable category weights to alleviate mild class imbalance. The experiment compared Logistic Regression, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF) and on the same preprocessing process Multilayer Perceptron (MLP). Among them, the accuracy rate of KNN is 0.918 and the AUC is 0.953. The recall rate of SVM is the highest (0.951); The F1 value of RF is the highest (0.913), and it can provide feature importance. Overall, traditional machine learning methods have achieved stable and interpretable diagnostic performance on this data, among which KNN and RF perform even better in combination. The research conclusion can provide a reference for the early clinical identification of high-risk individuals. Subsequently, it can be verified on larger, cross-center data, and further combined with interpretability methods to support clinical decision-making.

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.

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TY  - CONF
AU  - Daoyi Cheng
PY  - 2026
DA  - 2026/04/24
TI  - Heart Disease Prediction Using Machine Learning Models
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 636
EP  - 643
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_69
DO  - 10.2991/978-94-6239-648-7_69
ID  - Cheng2026
ER  -