Cardiovascular Disease Prediction Based on Machine Learning

Zhangyu Fan, Bohao Liu, Xiao Yan

2024

Abstract

In recent years, the incidence and mortality rates of cardiovascular diseases (CVD) have been increasing globally, showing characteristics of high prevalence, hospitalization, and mortality. Due to the multiple factors that contribute to CVD and the high cost of treatment, it is difficult for people to prevent and detect it in a timely manner. In this paper, the dataset of CVD from Kaggle is utilized to analyze and compare the factors that contribute to CVD using correlation analysis. After feature selection, six machine learning models, including regression models, decision tree models, random forest models, gradient boosting decision tree models, XGBoost models, and deep neural network models, are compared to find the model with the highest comprehensive efficiency in terms of accuracy, precision, recall, and other aspects as the prediction model. The results show that among various influencing factors, age, creatine phosphokinase levels, and troponin levels have a significant impact on CVD, and the decision tree model performs the best in CVD prediction

Paper Citation

in Harvard Style

Fan Z., Liu B. and Yan X. (2024). Cardiovascular Disease Prediction Based on Machine Learning. 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 404-411. DOI: 10.5220/0012939000004508

in Bibtex Style

@conference{emiti24,
author={Zhangyu Fan and Bohao Liu and Xiao Yan},
title={Cardiovascular Disease Prediction Based on Machine Learning},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={404-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012939000004508},
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 - Cardiovascular Disease Prediction Based on Machine Learning
SN - 978-989-758-713-9
AU - Fan Z.
AU - Liu B.
AU - Yan X.
PY - 2024
SP - 404
EP - 411
DO - 10.5220/0012939000004508
PB - SciTePress