Transparent Machine Learning for Predicting Early-Onset Diabetes

Abstract

Predicting early-onset diabetes through transparent machine learning models is crucial for proactive healthcare management. This abstract explores the significance of transparency in machine learning approaches, focusing on their application in identifying individuals at risk of developing diabetes before symptoms manifest. By leveraging interpretable models like decision trees, logistic regression, and rule-based classifiers, this study aims to provide clear insights into the predictive factors such as BMI, blood glucose levels, and genetic predisposition. These models not only enhance understanding of diabetes risk factors but also foster trust among healthcare providers by transparently outlining how predictions are made. Through this approach, early intervention strategies can be effectively tailored, potentially delaying or preventing the onset of diabetes and improving patient outcomes.