Predictive Analytics for Early Diabetes Detection with Interpretability

Abstract

This study explores the application of predictive analytics in early diabetes detection, emphasizing the critical role of interpretability in machine learning models. By leveraging transparent models such as decision trees, logistic regression, and rule-based classifiers, the research aims to identify high-risk individuals based on factors like age, BMI, blood pressure, and family history. These models provide clear, understandable insights into how specific features influence diabetes risk, facilitating trust and informed decision-making among healthcare providers and patients. Integrating additional data sources, including genomic data, environmental factors, and patient-reported outcomes, further enhances model accuracy and robustness. Collaborative efforts with healthcare providers ensure clinical validation and real-world applicability, supporting continuous model refinement. The study highlights the potential of interpretable machine learning to improve early detection rates, optimize preventive strategies, and ultimately, enhance patient outcomes in diabetes care.