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

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.

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.

From a publicly available dataset of over 400,000 real Salmonella enterica isolates, 10,000 samples were randomly selected, and biochip-like analog signals were synthetically generated from their AMR genotype profiles. KMeans clustering was employed for unsupervised subtype discovery, while supervised models including Random Forest, XGBoost, and a Voting Classifier were trained using fivefold stratified cross-validation. Model explainability was achieved via SHAP values, and Rule based recommendation system was designed to convert predictions into actionable, patient-level insights.

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.

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.