Aprendizado de Máquina para Predição de Diagnósticos de Doenças Cardiovasculares

• Francisco Romes da Silva Filho UFC
• Emanuel F. Coutinho UFC

DOI: https://doi.org/10.5753/sbcas.2022.222686

Abstract

Cardiovascular diseases (CVD) are adversities that affect the heart and blood vessels, being estimated in 2019 the cause of death of 17.9 million people. Due to data available in various forms, data analysis in medical informatics has gained importance, generating interest in producing analytical models oriented in Machine Learning (ML). CVD prediction is a complex challenge in the area of clinical data analysis, and classification with ML would play a significant role in heart disease prediction and research, to lessen impacts on the heart and prevent premature death. The objective of this work is to train and evaluate machine learning and deep learning models for predicting cardiovascular disease diagnoses.

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