Machine Learning for Enhanced Heart Disease Prediction: A Comprehensive Classifier Evaluation

Tonghui Wu

2024

Abstract

In recent decades, the growing recognition of the importance of preventing heart disease and identifying potential issues early has become paramount. Advances in machine learning (ML) technologies, fueled by the wealth of medical data, have emerged as essential tools in accurately forecasting cardiovascular diseases. This study aims to address the challenge of predicting heart disease with greater accuracy, an endeavor critical to the field of healthcare due to heart disease being a leading cause of mortality globally. Utilizing a comprehensive dataset sourced from a reputable cardiology database enriched with features reflecting mental health states such as degrees of depression, the study diverges from traditional models by incorporating these psychosocial factors. Extensive evaluation of twelve different ML classifiers, including Logistic Regression, Decision Trees, and Neural Networks, among others, was conducted to assess their performance in accurately predicting heart disease. The evaluation metric of choice was the F1 score, selected for its balance between precision and recall, particularly pertinent in medical diagnostics. Findings reveal that Logistic Regression outperformed other classifiers regarding accuracy, precision, recall, and F1 score. This supports the hypothesis that incorporating mental health indicators can enhance predictive models for heart disease. The study underscores the importance of considering both physiological and psychological factors in heart disease prediction and highlights the efficacy of ML techniques in navigating the complexities of healthcare diagnostics.

Paper Citation

in Harvard Style

Wu T. (2024). Machine Learning for Enhanced Heart Disease Prediction: A Comprehensive Classifier Evaluation. 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 633-638. DOI: 10.5220/0012961000004508

in Bibtex Style

@conference{emiti24,
author={Tonghui Wu},
title={Machine Learning for Enhanced Heart Disease Prediction: A Comprehensive Classifier Evaluation},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={633-638},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012961000004508},
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 - Machine Learning for Enhanced Heart Disease Prediction: A Comprehensive Classifier Evaluation
SN - 978-989-758-713-9
AU - Wu T.
PY - 2024
SP - 633
EP - 638
DO - 10.5220/0012961000004508
PB - SciTePress