HEART ATTACK PREDICTION USING MACHINE LEARNING: A COMPREHENSIVE SYSTEMATIC REVIEW AND BIBLIOMETRIC ANALYSIS

Gamboa-Cruzado J.; Crisostomo-Castro R.; Vilabuleje J.; López-Goycochea J.; Valenzuela J.N. · Journal of Theoretical and Applied Information Technology (2024)

Resumen

Studies on predicting heart attacks using Machine Learning demonstrate that there is a wide variety of algorithms and methodologies highlighting their impact on heart attack prediction. This can help in reducing the risk of lifestyle-related complications. To understand the current state of the art, a systematic literature review (SLR) was conducted from 2017 to 2021. A key step in this SLR was the search strategy, which identified 3,525 articles from various sources of information such as Taylor and Francis, IEEE Xplore, ARDI, ACM Digital Library, ProQuest, Wiley Online Library, and Microsoft Academic. Exclusion criteria were applied, such as articles older than five years, non-English articles, and papers not published in conferences or journals, to ensure only the most relevant studies were included, ultimately resulting in 82 articles. The findings from the systematic review focused predominantly on studies predicting heart attacks, detailing the best methodologies and algorithms used to enhance the accuracy of these predictions. The conclusions indicate that, despite different approaches, the articles exhibit common themes and objectives in achieving better heart attack predictions using Machine Learning. © Little Lion Scientific.

Instituciones: Universidad Nacional Mayor de San Marcos; Universidad de San Martín de Porres; Universidad Nacional Federico Villarreal; Universidad Autónoma del Perú