Predicting Diabetes Onset: Interpretable Machine Learning Solutions

Abstract

Predicting the onset of diabetes is crucial for early intervention and effective management of the disease. This study explores the application of interpretable machine learning solutions to enhance the transparency and trustworthiness of predictive models. Leveraging the Pima Indian Diabetes Dataset, comprehensive data preprocessing techniques are employed to handle missing values, normalize features such as glucose levels and BMI, and select clinically relevant predictors. Interpretable algorithms including decision trees, logistic regression, and rule-based classifiers are implemented to provide clear insights into the factors influencing diabetes risk. Techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are utilized to explain model predictions, highlighting the contribution of individual features. Visualizations further aid in understanding model outputs and decision-making processes. The findings underscore the importance of interpretable machine learning in healthcare, offering actionable insights for early diagnosis and personalized healthcare strategies aimed at mitigating diabetes onset.