Chronic liver disease classification using deep learning with SHAP-optimized hybrid features

The liver is a vital organ responsible for essential functions, including digestion, metabolism, detoxification, and immunity. Liver disorders, whether due to disease, injury, or congenital conditions, pose serious health risks and require timely diagnosis for effective treatment and improved survival. Advances in machine learning (ML), particularly deep learning, have demonstrated significant potential for disease prediction, offering clinicians more accurate and efficient diagnostic tools. In this study, we propose a novel predictive framework based on a deep neural network (DNN) integrated with feature ranking and projection-based algorithms for accurate liver disease detection. To enhance model interpretability, SHapley Additive exPlanations (SHAP) were applied to identify the most influential features affecting predictions. Experimental results indicate that the proposed DNN model outperforms traditional ML algorithms and state-of-the-art methods, achieving an average accuracy (ACC) of 92.50% under 10-fold cross-validation. These results emphasis its potential to improve diagnostic ACC, support early intervention, and enhance patient outcomes.