A consensus-weighted multi-agent ensemble feature selection framework for antibiotic susceptibility prediction in pediatric respiratory diseases

Background

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.