Biochip-simulated genotype signals enable accurate and interpretable AMR prediction via machine learning.

Background: Antimicrobial resistance (AMR) is an escalating global health crisis, driven by the rapid evolution of resistant pathogens and the limitations of traditional diagnostic methods. Current approaches such as culture-based techniques are time-intensive, while molecular methods demand specialized infrastructure. Objective: This study aims to develop a smart pathogen sensing framework using biochip-simulated genotypic signals combined with machine learning (ML) and explainable AI. The goal is to accurately predict AMR profiles while enabling model interpretability and personalized feedback through Agentic AI. Methods: From a publicly available dataset of over 400,000 real Results: The proposed Voting Classifier achieved superior multi-class prediction performance, with high accuracy, precision, recall, F1-score, and AUC across diverse resistance profiles. UMAP visualizations and silhouette scores confirmed robust clustering, while SHAP interpretation enhanced transparency by identifying key resistance genes. A rule-based recommendation system translated SHAP-ranked gene contributions into context-specific clinical insights, improving interpretability and practical usability. Comparative analysis with state-of-the-art studies highlighted the novelty and superiority of our biochip-integrated, explainable pipeline. Conclusion: This study presents a scalable, proof-of-concept diagnostic framework that integrates simulated biochip genotypes, interpretable ML models, and a rule-based recommendation system. By bridging predictive accuracy with actionable insights, the framework offers a pathway toward a potential pathway toward clinically relevant AMR diagnostics, advancing both computational innovation and practical decision support.