AI-Based Methods of Cardiovascular Disease Prediction and Analysis

Yifei Wang

2024

Abstract

Cardiovascular diseases (CVDs) remain a leading cause of global mortality, fuelling extensive medical research databases. Various models have been developed to predict CVDs from existing data, with machine learning (ML) emerging as a particularly effective method. This paper offers an overview of ML methods' performance in CVD prediction, specifically focusing on Random Forest (RF), Learning Vector Quantization (LVQ), and Naive Bayes (NB). Discrepancies among studies highlight the influence of factors such as data preprocessing, database selection, and sample size on ML performance. Consequently, determining the optimal ML method is challenging. This study lays the groundwork for future research, aiming to explore how each factor affects ML performance and facilitate improvements in subsequent studies. Furthermore, it encourages reproducibility through comprehensive literature review guidance. This paper lays foundation on future research into detailed influence of each factor on the performance of ML, and helps potential improvement for future studies. Reproductions are also hoped to be done with the guide of searched literatures.

Paper Citation

in Harvard Style

Wang Y. (2024). AI-Based Methods of Cardiovascular Disease Prediction and Analysis. 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 724-729. DOI: 10.5220/0012969800004508

in Bibtex Style

@conference{emiti24,
author={Yifei Wang},
title={AI-Based Methods of Cardiovascular Disease Prediction and Analysis},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={724-729},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012969800004508},
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 - AI-Based Methods of Cardiovascular Disease Prediction and Analysis
SN - 978-989-758-713-9
AU - Wang Y.
PY - 2024
SP - 724
EP - 729
DO - 10.5220/0012969800004508
PB - SciTePress