Prediction of Cardiovascular Diseases Based on Mainstream Machine Learning Algorithms

DOI

10.2991/978-94-6239-648-7_54How to use a DOI?

Keywords

Cardiovascular Diseases; Machine Learning; Predictive Model; K-Nearest Neighbors; Gradient Boosting

Abstract

Cardiovascular diseases (CVDs) are one of the hottest issues in present medical research due to their status as the leading cause of mortality worldwide. Studies have achieved certain achievements in early detection tool development. However, there is still a research gap in accurate, efficient and widely applicable predictive models for CVDs or models that could integrate multiple clinical indicators. This study attempts to predict cardiovascular diseases based on mainstream machine learning algorithms. Firstly, this study collected a clinical dataset including 11 feature variables (such as Age, Sex, ChestPainType, RestingBP) and a target variable (HeartDisease). Secondly, this study finished the dataset’s preprocessing and analysis. Finally, this study constructed and trained five mainstream machine learning models including Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machine (SVM). The experimental results show that the KNN model got the highest accuracy of 0.8913, recall of 0.9118 and F1-score of 0.9027, while the Gradient Boosting model got AUC of 0.94 and it was ranked first in generalization ability. This study concludes that mainstream machine learning algorithms, especially KNN and Gradient Boosting, could improve the accuracy of CVD prediction and could be used as an accurate and reliable tool in clinical early screening of cardiovascular diseases.

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  - Peiyuan Liu
PY  - 2026
DA  - 2026/04/24
TI  - Prediction of Cardiovascular Diseases Based on Mainstream Machine Learning Algorithms
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 491
EP  - 501
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_54
DO  - 10.2991/978-94-6239-648-7_54
ID  - Liu2026
ER  -