# Machine Learning Model for Predicting Multidrug Resistance in Clinical Klebsiella pneumoniae Isolates

## TL;DR

This paper introduces a machine learning model that can quickly predict antibiotic resistance in Klebsiella pneumoniae, helping doctors choose effective treatments faster.

## Contribution

The study introduces a novel application of random forest machine learning for rapid multidrug resistance prediction in Klebsiella pneumoniae.

## Key findings

- The random forest model achieved an average AUC of 0.96 in predicting resistance to 22 antibiotics.
- High accuracy was observed for critical antibiotics like Ertapenem (100%), Imipenem (93%), and Meropenem (95%).
- Machine learning models outperformed conventional methods in speed and accuracy of resistance prediction.

## Abstract

Background/Objectives: Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship.