Transparency in Prediction: Understanding Early Diabetes with Interpretable ML Models

Abstract

Early detection of diabetes is crucial for effective management and prevention of complications. Machine learning (ML) models offer promising avenues for predicting diabetes risk, yet their black-box nature often hinders adoption in clinical settings due to a lack of transparency and interpretability. In this study, the use of interpretable ML models to enhance transparency in diabetes prediction is explored. Leveraging a diverse dataset comprising clinical and demographic features, the efficacy of interpretable ML models in understanding the early signs of diabetes is demonstrated. Results show that these models not only achieve high predictive accuracy but also provide transparent insights into the underlying factors contributing to diabetes risk. By elucidating the rationale behind predictions, interpretable ML models empower healthcare practitioners with actionable information to tailor preventive interventions effectively. This study underscores the importance of transparency in ML-based prediction models and highlights the potential of interpretable ML approaches in improving early diabetes detection and management strategies. Results show that these models not only achieve high predictive accuracy but also provide transparent insights into the underlying factors contributing to diabetes risk. By elucidating the rationale behind predictions, interpretable ML models empower healthcare practitioners with actionable information to tailor preventive interventions effectively. This study underscores the importance of transparency in ML-based prediction models and highlights the potential of interpretable ML approaches in improving early diabetes detection and management strategies.