K. V

An interpretable machine learning framework for early-stage diabetes mellitus prediction using comparative classification models and SHAP

A machine learning-based framework enhanced with explainability is introduced, built around a structured data preparation process that handles categorical encoding, numerical scaling, and minority class oversampling through the SMOTE technique, positioning it as a trustworthy tool for assisting medical professionals in data-driven clinical decision-making.