Predictive modeling of pediatric drug-induced liver injury: Dynamic classifier selection with clustering analysis.

Background: Pediatric populations are more vulnerable to drug-induced liver injury (DILI) due to distinct pharmacokinetic profiles and ongoing physiological maturation processes. However, early identification and assessment of DILI in pediatric patients present significant clinical challenges, primarily due to the inherent complexity of pediatric cases and substantial limitations in available clinical data. Objective: This study introduces a framework that integrates clustering analysis with dynamic classifier selection (DCS) techniques to enhance pediatric DILI prediction. The proposed method addresses challenges such as patient heterogeneity and class imbalance, while optimizing predictive performance to support clinical decision-making. Methods: We investigated a retrospective cohort of 12,555 pediatric inpatients across six hospitals in Chongqing, China. The dataset encompassed a wide range of biomedical parameters, including laboratory results and liver function profiles, along with clinical documentation spanning demographic characteristics, medical histories, and medication regimens. Patients were stratified into four distinct clinical subgroups based on silhouette coefficient. A diverse pool of base classifiers was generated with varied initialization strategies and hyperparameter optimizations tailored to each patient cluster. The classification process was further refined through the implementation of Dynamic Classifier Selection with Multiple Classifier Behavior (DCS-MCB) methodology, which adaptively customizes model selection based on the distinctive clinical profiles of each subgroup. Results: The Clustering-enhanced DCS-MCB framework demonstrated superior performance compared to conventional machine learning models across evaluation metrics. The ensemble learning models consistently outperformed individual classifier models, with the presented study achieving the highest F1-score (0.926), MCC (0.917), G-mean (0.959), demonstrating the strength of this hybrid approach in addressing the complexities of pediatric DILI prediction. Conclusion: The integration of clustering analysis with dynamic classifier selection has demonstrated efficacy in complex real-world clinical settings. This methodology provides a more robust, precise, and clinically adaptable framework for patient stratification and drug safety surveillance.