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.