Machine Learning Solutions for Heart Disease Diagnosis: Model Choices and Factor Analysis

Hao Rui

2025

Abstract

The prediction of heart disease diagnosis is of paramount importance since it is one of the leading causes of death nowadays. Yet accurately identifying and monitoring the factors relative to heart diseases pose significant challenges. Traditional visual survey methods are time-consuming, often hampered by the lack of data exchange among hospitals and individual doctors, and may not provide real-time data crucial for effective prevention strategies. Machine learning (ML) technologies have become increasingly potent instruments in the diagnosis of diseases in recent years. In this project, several models were investigated. A complete set of data gathered from Long Beach V, Cleveland, Hungary, and Switzerland was utilized. This data set, which encompasses over 13 different factors that are relative to heart diseases, is meticulously preprocessed to ensure data quality. The outcomes not only show how well machine learning techniques can anticipate heart conditions, but they also open the door for the creation of edge computing and mobile applications. These might be installed in far-off places, giving physicians and hospitals access to real-time data and enabling timely treatment decisions. Thus, this study marks a substantial advancement in the use of cutting-edge technologies for the identification of heart disease in real time.

Paper Citation

in Harvard Style

Rui H. (2025). Machine Learning Solutions for Heart Disease Diagnosis: Model Choices and Factor Analysis. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 60-66. DOI: 10.5220/0013678100004670

in Bibtex Style

@conference{icdse25,
author={Hao Rui},
title={Machine Learning Solutions for Heart Disease Diagnosis: Model Choices and Factor Analysis},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={60-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013678100004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Machine Learning Solutions for Heart Disease Diagnosis: Model Choices and Factor Analysis
SN - 978-989-758-765-8
AU - Rui H.
PY - 2025
SP - 60
EP - 66
DO - 10.5220/0013678100004670
PB - SciTePress