Aprendizado de Máquina Aplicado à Predição de Doenças Cardiometabólicas com Utilização de Indicadores Metabólicos e Comportamentais de Risco à Saúde

Cardiometabolic diseases, developed throughout the worker’s life, such as hypertension, diabetes, dyslipidemia and obesity are among the main causes of death and are associated with modifiable and controllable risk factors. The general objective of this study was to apply supervised Machine Learning techniques and to compare their performance to predict the risk of developing cardiometabolic disease from servers working at the School Hospital of south in Brazil. We sought to map the characteristics of individuals who are more likely to develop cardiometabolic diseases. The machine learning models evaluated were Naive Bayes, Decision Tree, Random Forest, KNN, Logistic Regression and SVM. The results obtained in the experiments showed that some supervised machine learning models produce a good classification, depending on the attributes and hyperparameters used.