Using Interpretable Machine Learning to Detect Early Signs of Diabetes

Abstract

The early detection of diabetes is crucial for effective intervention and management, potentially preventing severe health complications. This study explores the application of interpretable machine learning models to detect early signs of diabetes, emphasizing transparency and understandability for healthcare professionals. Traditional black-box models, while accurate, often lack the interpretability required for clinical decision-making. Interpretable models such as decision trees, logistic regression, and rule-based classifiers are employed to predict the onset of diabetes using patient data. Various feature importance techniques are leveraged to ensure the models provide clear insights into which factors most significantly contribute to the risk of developing diabetes. Results demonstrate that interpretable models can achieve competitive performance with traditional black-box approaches while offering the added benefit of transparency. This transparency not only aids in building trust with healthcare providers but also facilitates better patient communication and personalized treatment plans. Ultimately, this research highlights the potential of interpretable machine learning as a valuable tool in the early detection and management of diabetes, contributing to improved patient outcomes and more informed healthcare practices.