Machine Learning Classification in Cardiology: A Systematic Mapping Study

Khadija Anejjar, Fatima Amazal, Ali Idri

2024

Abstract

Heart disease, a widespread and potentially life-threatening condition affecting millions globally, demands early detection and precise prediction for effective prevention and timely intervention. Recently, there has been a growing interest in leveraging machine learning classification techniques to enhance accuracy and efficiency in the diagnosis, prognosis, screening, treatment, monitoring, and management of heart disease. This paper aims to contribute through a comprehensive systematic mapping study to the current body of knowledge, covering 715 selected studies spanning from 1997 to December 2023. The studies were meticulously classified based on eight criteria: year of publication, type of contribution, empirical study design, type of medical data used, machine learning techniques employed, medical task focused on, heart pathology assessed, and classification type.

Paper Citation

in Harvard Style

Anejjar K., Amazal F. and Idri A. (2024). Machine Learning Classification in Cardiology: A Systematic Mapping Study. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 409-416. DOI: 10.5220/0012785600003756

in Bibtex Style

@conference{data24,
author={Khadija Anejjar and Fatima Amazal and Ali Idri},
title={Machine Learning Classification in Cardiology: A Systematic Mapping Study},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={409-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012785600003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Machine Learning Classification in Cardiology: A Systematic Mapping Study
SN - 978-989-758-707-8
AU - Anejjar K.
AU - Amazal F.
AU - Idri A.
PY - 2024
SP - 409
EP - 416
DO - 10.5220/0012785600003756
PB - SciTePress