An Interpretable Deep Learning Model for Early Disease Diagnosis Leveraging SHAP Explanations

Early and accurate disease diagnosis plays a pivotal role in preventive healthcare, significantly contributing to the reduction of patient morbidity, mortality, and overall healthcare costs. Timely identification of disease risk enables early intervention, personalized treatment planning, and improved patient outcomes. With the exponential growth of electronic health records (EHRs), medical imaging data, laboratory reports, and wearable sensor information, data-driven approaches—particularly deep learning (DL) models—have emerged as powerful tools for clinical prediction and diagnostic support. These models are capable of capturing complex, nonlinear relationships among high-dimensional medical features, often outperforming traditional statistical and machine learning techniques in terms of predictive accuracy. Despite their superior performance, deep learning models are frequently criticized for operating as “black boxes.” Their complex internal architectures, involving multiple hidden layers and millions of parameters, make it difficult to interpret how specific input features influence the final prediction. In high-stakes domains such as healthcare, this lack of transparency poses significant challenges. Clinicians are understandably hesitant to rely on automated systems without clear explanations, as diagnostic decisions directly impact patient safety and treatment outcomes. Furthermore, regulatory frameworks and ethical guidelines increasingly emphasize the need for transparency, accountability, and interpretability in AI-driven medical systems. To address these limitations, this paper proposes an interpretable deep learning framework for early disease diagnosis that integrates SHapley Additive exPlanations (SHAP) to enhance model transparency. SHAP is a game-theoretic approach to explainable artificial intelligence that quantifies the contribution of each input feature to an individual prediction. By computing Shapley values, the framework provides both global interpretability (understanding overall feature importance across the dataset) and local interpretability (explaining individual patient-level predictions). This dual-level explanation mechanism bridges the gap between high predictive performance and clinical interpretability. The proposed framework is evaluated on multiple benchmark medical datasets representing different disease conditions. Experimental results demonstrate that incorporating SHAP explanations does not compromise predictive accuracy while significantly improving the transparency and interpretability of the model’s decisions. The findings suggest that combining deep learning with SHAP-based explainability enhances clinician trust, facilitates feature-level insights into disease risk factors, and supports evidence-based medical decision making. Ultimately, this approach contributes toward the development of reliable, transparent, and clinically applicable AI systems for early disease detection.