Interpretable Algorithms for Early Diagnosis of Diabetes

Abstract

This abstract explores the application of interpretable machine learning algorithms in the early diagnosis of diabetes, addressing the need for transparent and actionable insights in healthcare decision-making. Interpretable models such as decision trees, logistic regression, and rule-based classifiers are utilized to analyze diverse patient data, including genetic predispositions, lifestyle factors, and clinical indicators. These models provide clear explanations of the factors influencing diabetes risk, facilitating informed clinical interventions and patient education. By integrating explainable AI techniques like LIME and SHAP, the study enhances the transparency of model predictions, ensuring clinicians and patients understand and trust the diagnostic outcomes. The research highlights the potential of interpretable algorithms to improve early detection rates, optimize healthcare resource allocation, and enhance patient outcomes in diabetes management.