Heart Disease Prediction Using Gradient Boosting Decision Trees

Yuzhe Wu

2024

Abstract

Heart disease is one of the major health challenges globally, and its prevention and treatment are crucial for ensuring people's health. This study is based on the 2020 stacked ensemble survey dataset for heart disease classification. By analyzing the relationship between various factors and heart disease, we explore the application of machine learning models in heart disease prediction. The study found that factors such as fasting blood sugar, cholesterol, and exercise-induced angina are closely related to heart disease, while the influence of resting electrocardiogram and resting blood pressure is relatively small. Among various machine learning models compared, Gradient Boosting Decision Trees (GBDT) performed the best, with high prediction accuracy and precision. However, the study also points out the limitations of the dataset and the issue of models not fully unleashing their potential.It is worth noting that this study also explores the possibility of using other machine learning models in heart disease prediction and conducts comparative analysis, providing more references for heart disease prevention and treatment.

Paper Citation

in Harvard Style

Wu Y. (2024). Heart Disease Prediction Using Gradient Boosting Decision Trees. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 527-535. DOI: 10.5220/0012958300004508

in Bibtex Style

@conference{emiti24,
author={Yuzhe Wu},
title={Heart Disease Prediction Using Gradient Boosting Decision Trees},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={527-535},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012958300004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Heart Disease Prediction Using Gradient Boosting Decision Trees
SN - 978-989-758-713-9
AU - Wu Y.
PY - 2024
SP - 527
EP - 535
DO - 10.5220/0012958300004508
PB - SciTePress