Brandon GRADOS-ALVAREZ1, Javier GAMBOA-CRUZADO1, Norma GÁLVEZ-DÍAZ2, Guillermo PAUCAR-CARLOS3, Carlos ALMIDÓN ORTIZ4 and Renzo CAÑOTE FLORES5
1Universidad Autónoma del Perú, Perú
2Universidad Señor de Sipán S.A.C., Perú
3Universidad Nacional de San Antonio Abad del Cusco, Perú
4Universidad Nacional de Huancavelica, Perú
5Clínica de Ojos Oftalmosalud, Perú
Cardiovascular diseases (CVDs) are “the leading cause of death” according to WHO (2017) and the Organization for Economic Cooperation and Development (OECD), which points out that the “rate of obesity and diabetes is on the rise”, and that by 2030, 180 million people will be at risk of CVDs (2015). For this reason, the demand for cardiac diagnostics in health care facilities has increased significantly. Hospitals have improved diagnostic processes owing to their influence on the time, cost, and efficiency of medical care. In this study, a systematic review of the literature was carried out on the subject of machine learning in cardiac diagnostics. Articles related to the research topic were consulted to answer questions raised as the first step of the systematic review method. The review has shown the relevant trends in medical precision and revealed the most appropriate and effective machine learning techniques in cardiac diagnosis.