Explainable AI For Comprehensive Disease Detection

[1]. The increasing integration of Artificial Intelligence (AI) in healthcare presents transformative opportunities for disease diagnosis and prediction. However, the inherent opacity of many machine learning models introduces a critical challenge, hindering the necessary trust and adoption by healthcare professionals. This project addresses this gap by focusing on the development of interpretable and transparent AI models for predicting two significant medical conditions: Type 2 Diabetes and Heart Disease. Our approach utilizes explainable AI (XAI) techniques—specifically, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations)—to provide healthcare practitioners with actionable insights into the models’ decision-making processes. By applying SHAP for global and local explanations in the diabetes model and LIME for the heart disease model, our work demonstrates how feature importance and risk factors can be clearly communicated, leading to a deeper understanding of disease outcomes and more informed clinical decision support. This research not only achieves high prediction accuracy but also emphasizes the critical role of model transparency in fostering confidence in AI-driven healthcare diagnostics. This approach will result in trust, personalized treatment plans and better patient outcomes. Furthermore, to facilitate accessibility and improve user engagement we also include a text to speech functionality. This research thereby provides a pathway for adoption of transparent AI techniques in the healthcare practice.