A consensus-weighted multi-agent ensemble feature selection framework for antibiotic susceptibility prediction in...

Antibiotic susceptibility prediction is important because of the risks of failing to respond to antibiotics and the risk of antimicrobial resistance when treating respiratory diseases in children. Machine learning models can represent complex relationships among clinical and microbiological data; however, their effectiveness is strongly dependent on interpretability, stability, and methodological robustness. Redundancy-aware consensus-based feature selection frameworks provide a reliable way to enhance predictive performance and clinical usability.

Preprocessing, balancing, leakage-controlled splitting of integrated pediatric clinical and nasopharyngeal antibiogram data, and binary antibiotic susceptibility prediction were conducted. Consensus scoring, vote strength, and redundancy penalization were used to create a model by combining six feature selection agents: Random Forest importance, Gradient Boosting importance, ANOVA F-test, Mutual Information, RFE-Random Forest and RFE-Gradient Boosting. A total of twelve classifiers were tested, and the three best classifiers were selected using a genetic algorithm for soft-voting ensemble construction. Standard metrics, cross-validation, statistical tests, and SHAP explainability were employed to validate model performance.

In predicting antibiotic susceptibility, LightGBM achieved the highest accuracy of 0.8718, the highest F1-score of 0.8726, and the highest ROC-AUC of 0.9498. Optimal models for soft voting ensemble construction were identified using a genetic algorithm: Decision Tree, XGBoost, and LightGBM. The ensemble model attained an accuracy of 0.8799, recall of 0.8907, specificity of 0.8692, F1-score of 0.8811, and ROC-AUC of 0.9490.

The proposed consensus-based multi-agent feature selection method successfully selected a compact, non-redundant, and clinically interpretable predictor subset for predicting antibiotic susceptibility in pediatric patients. Antibiotic identity and bacterial organism were primary factors in SHAP analysis, with clinical, inflammatory, environmental, and hospital-related factors also playing significant roles. The framework offers a meaningful and statistically sound machine learning tool for studies in pediatric antimicrobial decision support.