Interpretable Deep Learning Models for Improved Diabetes Diagnosis

Interpretable Deep Learning Models for Improved Diabetes Diagnosis

Vadada Yamuna,Durvasula Venkata Sai Bhavana,Amiti Sudhish,Animireddy Tharunima

Introduction:

Diabetes, a chronic condition marked by persistent high blood sugar, poses major global health challenges due to complications like cardiovascular disease and neuropathy. Traditional diagnostic methods, though common, are invasive, time-consuming, and prone to interpretation errors. To overcome these issues, this project proposes a novel machine learning framework that integrates structured data (e.g., demographics, test results) and unstructured data (e.g., retinal images, clinical notes) using deep learning models like CNNs, RNNs, and transformers. Explainable AI techniques, such as SHAP and attention mechanisms, are incorporated to make predictions transparent and trustworthy. An interactive diagnostic tool allows clinicians to explore model insights, enhancing adoption in real-world settings. With continuous learning capabilities, this framework aims to improve diagnostic accuracy, personalize treatment, and reduce healthcare burdens.