An AI-Driven Two-Stage Feature Fusion-Based Ensemble Model for Liver Disorder Prediction

The liver plays a crucial role in maintaining essential physiological functions; however, excessive alcohol consumption significantly increases the risk of liver disorders. Accurate and early prediction of such conditions is vital for timely intervention and effective clinical management. Nevertheless, liver disorder prediction is typically challenged by class imbalance, which makes the conventional fixed classification threshold (0.5) suboptimal for binary classification. To address these issues, this paper proposes an AI-driven two-stage feature fusion-based ensemble model for intelligent biomedical data processing and liver disorder prediction. In the first stage, multiple tree-based ensemble models are employed to evaluate feature importance, and a feature selection strategy is designed to select an optimal subset of discriminative features. In the second stage, prediction probabilities generated by these base learners are integrated with the selected feature subset to construct an enhanced feature space through feature-level information fusion. This probability-aware fusion strategy captures richer predictive information and alleviates the limitations of fixed-threshold binary classification. In addition, a meta-ensemble model is employed to aggregate heterogeneous predictive patterns from multiple learners for the final prediction based on an optimized threshold. Extensive experiments based on two benchmark liver disorder datasets demonstrate that the proposed model consistently outperforms the compared models in terms of predictive performance. Statistical analysis also confirms that the proposed model significantly outperforms the compared methods. These results indicate that the proposed model could serve as a useful AI-aided biomedical healthcare data processing tool for liver disorder prediction.