Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets

Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.

A novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. The framework integrates feature engineering and resampling strategies to enhance predictive accuracy.

Rigorous testing was conducted on three datasets-PIMA, Diabetes Dataset 2019, and BIT_2019-demonstrating the robustness and adaptability of the methodology across varying data environments.

The experimental results highlight the critical role of model selection and imbalance mitigation in achieving reliable and generalizable diabetes predictions. This study offers significant contributions to medical informatics by proposing a robust data-driven framework that addresses class imbalance challenges, thereby advancing diabetes prediction accuracy.