Authors:
Khadija Anejjar
1
;
Fatima Amazal
1
and
Ali Idri
2
Affiliations:
1
LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, BP 8106, 80000 Agadir, Morocco
;
2
Faculty of Medical Sciences, Mohammed VI Polytechnic University, Morocco
Keyword(s):
Heart Disease, Machine Learning, Classification Techniques, Predictive Models.
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.