Hybrid decision tree-based machine learning models for diabetes prediction

Due to the ever-increasing incidence of diabetes, effective screening strategies are needed for early diagnosis and intervention. This study proposes a novel approach that harnesses the power of artificial intelligence (AI) to predict diabetes risk. Using machine learning techniques and a database with demographic, clinical and lifestyle variables, the proposed model achieves the best accuracy in predicting the probability of developing diabetes. The prediction model uses advanced feature selection and cross-validation techniques to improve reliability and generalizability. Integrating AI into diabetes prediction paves the way for earlier healthcare, enabling personalized intervention and ultimately reducing the burden of diabetes on individuals and healthcare systems.