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

## TL;DR

This paper introduces a machine learning framework that uses simulated biochip signals to predict antimicrobial resistance with high accuracy and interpretability.

## Contribution

The novel integration of biochip-simulated genotypes with explainable AI and a rule-based recommendation system for AMR prediction.

## Key findings

- The Voting Classifier achieved high accuracy, precision, recall, F1-score, and AUC for AMR prediction.
- SHAP values identified key resistance genes, enhancing model transparency and interpretability.
- UMAP and silhouette scores confirmed robust clustering of resistance profiles.

## Abstract

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.