Machine learning techniques comparison for risk assessment of cardiovascular disease development by health indicators / Comparação de técnicas de aprendizagem de máquinas para avaliação de risco de desenvolvimento de doença cardiovascular por indicadores de saúde

Abstract

Currently, one of the leading causes of death around the world are caused by diseases or acute syndromes installed in the cardiovascular system of the human body. Thus, this paper presents a modern alternative for the detection of cardiovascular diseases from health indicators such as age, gender, glucose and cholesterol indices, used as inputs for machine learning systems. The evaluation is made by using supervised learning algorithms, such as K-Nearest Neighbours, Decision Tree, Logistic Regression, Voting Classification, from the accuracy observed during the testing period, in order to conclude what is the best alternative for the construction of an effective cardiovascular event predictor in the clinical routine.