The liver performs numerous essential metabolic and regulatory functions; however, chronic alcohol consumption remains a major cause of hepatic disease. Consequently, the early and accurate prediction of alcohol-related liver disorders is of considerable clinical importance. A key challenge in this task is the class imbalance present in standard liver disorder datasets, which renders the default classification threshold of 0.5 suboptimal for minority-class prediction performance. To address this issue, we propose a swarm intelligence-enhanced framework that integrates the Butterfly Optimization Algorithm (BOA) with CatBoost. Unlike conventional approaches that either tune hyper-parameters alone or adjust classification thresholds as a post-processing step, the proposed method simultaneously optimizes both CatBoost hyper-parameters and the classification threshold. By incorporating threshold optimization into the BOA objective function, the model adaptively balances sensitivity and specificity without altering the original class distribution. Experiments conducted on two benchmark liver disorder datasets demonstrate that the proposed BOA-driven dual optimization consistently improves performance over baseline classifiers across multiple evaluation metrics. These results demonstrate that joint hyper-parameter and threshold optimization provides an effective and computationally efficient approach for imbalanced liver disorder prediction.
Weng et al. (Fri,) studied this question.