Models of identification cardiovascular diseases implementing machine learning techniques: a systematic literature review/Modelos de identificacion de enfermedades cardiovasculares implementando tecnicas de aprendizaje maquina: una revision sistematica de la literatura

The use of Machine Learning (ML) techniques in the health area, specifically in the identification of cardiovascular diseases (IEC), has had a significant impact due to the ability to analyze large amounts of data and extract relevant information that can be essential for medical decision-making. However, before making them available to end users (doctors), their abihty to detect heart disease-related symptomatology should be evaluated using benchmark data sets in experimental settings. Therefore, determining which features to use in the evaluation process and which ML techniques are most suitable for IEC prediction is complicated. This article presents a systematic literature review on processing cardiovascular disease clinical trial-based datasets and ML techniques. In this sense, the different variables were analyzed from journal publications indexed in specialized databases such as Scopus, Web of Science, Science Direct, Biomed, and Pubmed.