Inteligência artificial para predição de diabetes mellitus tipo 2 com base em dados sociodemográficos e estilo de vida

Abstract

Non-communicable chronic diseases, especially type 2 Diabetes Mellitus (T2DM), represent one of the greatest global public health challenges due to their high prevalence and their economic and social impact. In this context, this study applied Artificial Intelligence (AI) techniques, with an emphasis on Machine Learning (ML), to predict T2DM risk based on sociodemographic, clinical, and lifestyle data. Two public datasets from the Behavioral Risk Factor Surveillance System (BRFSS) were used, comprising 250,360 records and 21 variables. The J48 (C4.5) algorithm was implemented using Weka 3.8.6 software with 10-fold cross-validation. The model achieved an average accuracy of 83.85% for binary classification and 88.84% for multiclass classification. Feature selection identified six most relevant variables: hypertension, high cholesterol, heart disease, excessive alcohol consumption, self-rated health, and difficulty walking. The results demonstrate the potential of AI techniques for the early identification and prevention of T2DM, reinforcing the importance of integrating clinical, nutritional, and behavioral data in the development of predictive models. It is concluded that the application of AI in precision nutrition can optimize monitoring and preventive diagnosis, reducing costs and promoting quality of life.