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stackPredAMR-a stacked random forest approach improves AMR phenotype prediction for multiple species and

Julian Welling^1,2, Miriam Balzer^1,2, Leah Consten¹

¹Department of Medicine, University of Duisburg-Essen, Essen, 45147, Germany.

Bioinformatics Advances

|July 9, 2026

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A new machine learning model, stackPredAMR, accurately predicts antimicrobial resistance in key bacterial pathogens using whole genome sequencing data. This rapid method offers a significant advancement over traditional testing for combating antimicrobial resistance.

Area of Science:

Microbiology
Bioinformatics
Machine Learning

Background:

Antimicrobial resistance (AMR) is a critical global health challenge.
Current antimicrobial susceptibility testing (AST) methods are slow due to reliance on bacterial cultivation.
Whole genome sequencing (WGS) with machine learning (ML) presents a faster alternative but faces limitations in scope and data.

Purpose of the Study:

To develop and validate a novel ML framework, stackPredAMR, for predicting AMR.
To enhance prediction accuracy by incorporating antimicrobial resistance gene presence and cross-resistance patterns.
To provide a scalable and extensible solution for rapid AMR detection.

Main Methods:

Developed stackPredAMR, an ML framework utilizing a stacked architecture with random forest layers.

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Input features include antimicrobial resistance gene presence.

Model trained and benchmarked on over 2500 WGS datasets linked to phenotypic resistance data for *Escherichia coli*, *Klebsiella pneumoniae*, and *Acinetobacter baumannii*.

Main Results:

stackPredAMR achieved high performance metrics: median accuracy of 0.94, ROC AUC of 0.97, and F1-score of 0.91.
The model demonstrated superior performance compared to existing methods.
Predicted resistance to 18 antimicrobial agents across three bacterial species.

Conclusions:

stackPredAMR offers a rapid, accurate, and cost-effective method for predicting antimicrobial resistance.
The framework's design supports future expansion to more species and antimicrobial agents.
Freely available source code and datasets facilitate adoption and further research.