<![CDATA[Chronic hepatitis continues to pose a significant global health challenge, frequently advancing to liver cirrhosis and hepatocellular carcinoma if not managed with precise prognostic interventions. The capacity to accurately predict patient survival is essential for optimizing resource allocation and treatment planning. Although Machine Learning (ML) has shown promise in medical diagnostics, standard algorithms often underperform when applied to hepatitis datasets characterized by severe class imbalance and high dimensionality. Conventional models tend to bias predictions toward the majority class (survival), resulting in a high rate of False Negatives for the minority class (mortality), which is clinically unacceptable. Moreover, single-classifier approaches often lack the generalization capability necessary for robust clinical deployment. To address these deficiencies, this study proposes a Hybrid Cost-Sensitive Stacking Ensemble Model (HCS-SEM). The framework integrates three strategic components: (1) a rigorous Split-First Synthetic Minority Oversampling Technique (SMOTE) protocol to resolve class skewness without data leakage; (2) a Chi-Square feature ranking mechanism to eliminate redundant clinical attributes; and (3) a Two-Tier Stacking Architecture employing Random Forest, SVM, and Gradient Boosting as base learners, optimized by a Logistic Regression meta-learner. Experimental validation on the UCI Hepatitis dataset demonstrates that HCS-SEM significantly outperforms standalone classifiers and traditional ensemble methods. The model achieves superior performance metrics, particularly in Sensitivity and F1-Score, confirmed by the Friedman Rank Test and Nemenyi post-hoc analysis. These findings suggest that the proposed HCS-SEM provides a robust, clinically viable tool for hepatitis prognosis, offering high-precision decision support for medical practitioners managing high-risk patients.]]>
