Comparative Analysis of Machine Learning Models for Heart Disease Prediction

Weichi Gao

2024

Abstract

In recent years, heart disease has become one of the major public health problems worldwide. According to the World Health Organization, cardiovascular disease is one of the leading causes of death, especially among middle-aged and elderly people. Lifestyle changes, such as an unhealthy diet, lack of exercise, and high stress levels, dramatically increase the incidence of heart disease. This paper will compare three models, including decision trees, random forests, and Limit Gradient Lift (XGBoost), by analyzing heart disease data sets. Through the comparison and analysis of these three machine learning models, the final conclusion is that XGBoost model has the highest accuracy. Machine learning has significant advantages in the medical field, especially in the detection of heart disease. First, machine learning algorithms can efficiently process large amounts of data. Second, machine learning is able to identify complex patterns and small differences that are difficult to detect with traditional methods, thus improving the accuracy of diagnosis. In addition, machine learning is highly adaptive, with the ability to continuously optimize and improve models based on new data.

Paper Citation

in Harvard Style

Gao W. (2024). Comparative Analysis of Machine Learning Models for Heart Disease Prediction. In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM; ISBN 978-989-758-738-2, SciTePress, pages 234-237. DOI: 10.5220/0013296800004558

in Bibtex Style

@conference{mlscm24,
author={Weichi Gao},
title={Comparative Analysis of Machine Learning Models for Heart Disease Prediction},
booktitle={Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM},
year={2024},
pages={234-237},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013296800004558},
isbn={978-989-758-738-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM
TI - Comparative Analysis of Machine Learning Models for Heart Disease Prediction
SN - 978-989-758-738-2
AU - Gao W.
PY - 2024
SP - 234
EP - 237
DO - 10.5220/0013296800004558
PB - SciTePress