An Explainable Deep Learning Framework for Accurate and Transparent Cancer Prediction Using Medical Data

Cancer diagnosis and prognosis remain critical challenges in precision medicine due to the complexity and heterogeneity of tumor data. This study presents a deep learning-based framework integrated with Explainable Artificial Intelligence (XAI) to achieve accurate, interpretable, and trustworthy cancer prediction. The proposed model combines Convolutional Neural Networks (CNN) for feature extraction with Explainability tools such as SHAP (SHapley Additive exPlanations) and Grad-CAM (Gradient weighted Class Activation Mapping) to visualize and interpret model decisions. Experiments were conducted using the Breast Cancer Wisconsin (Diagnostic) dataset and validated across multiple performance metrics, including accuracy, precision, recall, and F1-score. The proposed framework achieved a prediction accuracy of 98.2%, outperforming traditional classifiers such as Support Vector Machines and Random Forests. The integration of XAI provided detailed insights into feature contributions, ensuring model transparency and enhancing clinical interpretability. This study demonstrates how explainable deep learning can enhance cancer diagnosis systems, bridging the gap between black-box AI models and clinical decision support. The framework can be extended to other cancer types, paving the way for trustworthy and data-driven healthcare analytics.