Prediction of Heart Failure Occurrence Based on the Categorical Boosting Model

Yinan Gao

2024

Abstract

Cardiovascular disease (CVD) is the world's greatest cause of mortality, taking an estimated 17.9 million lives annually and contributing 31% of all fatalities worldwide. Heart failure is a prevalent CVD-related occurrence and has a five-year survival rate similar to malignant tumors, at around 50%. Therefore, the prevention and advanced Interfere treatment are the key to current research. This study aims to predict heart failure occurrence based on various indicators of patients' physical health by constructing a Categorical Boosting machine learning model. The final trained model achieved a prediction accuracy of 88.13%, which fully validates the feasibility of using this model for practical heart failure prediction. Therefore, the primary focus of this research is to continue optimizing this model in the future, promote clinical validation, and facilitate its practical application. By identifying more potential patients, conducting early diagnosis and treatment, and effectively reducing the incidence of heart failure disease, we aim to realize the actual application of machine learning technology in the medical field.

Paper Citation

in Harvard Style

Gao Y. (2024). Prediction of Heart Failure Occurrence Based on the Categorical Boosting Model. 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 22-29. DOI: 10.5220/0012888900004508

in Bibtex Style

@conference{emiti24,
author={Yinan Gao},
title={Prediction of Heart Failure Occurrence Based on the Categorical Boosting Model},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={22-29},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012888900004508},
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 - Prediction of Heart Failure Occurrence Based on the Categorical Boosting Model
SN - 978-989-758-713-9
AU - Gao Y.
PY - 2024
SP - 22
EP - 29
DO - 10.5220/0012888900004508
PB - SciTePress