Work

A machine learning and neural network approach for classifying multidrug-resistant bacterial infections

Year: 2025

Type: article

Abstract: Antimicrobial resistance (AMR) represents a major public health challenge, significantly complicating infection prevention and treatment. This study employs machine learning and neural network techniques to classify multidrug-resistant Gram-negative bacterial (MDR-GNB) infections using electronic health records from 624 patients at Thatphanom Crown Prince Hospital in Thailand. We compared several algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Light Gradient Boosting Machine (LightGBM), with the MLP model exhibiting the highest accuracy and specificity. Performance was further enhanced by integrating feature selection methods such as Sequential Forward Selection (SFS), Recursive Feature Elimination with Cross-Validation (RFE-CV), and SelectKBest with data augmentation techniques, including ADASYN and SMOTE variants. Utilizing SHapley Additive exPlanations (SHAP) provided valuable insights into the most influential predictors for MDR-GNB. Notably, the MLP model achieved an AUC of 0.70, surpassing prior studies and highlighting its potential to advance clinical decision-making in managing MDR-GNB infections. • Leverage existing methods to improve model performance in classifying multidrug-resistant Gram-negative bacterial infections. • Combine multiple algorithms, feature selection, and data augmentation to achieve optimal results, surpassing conventional models in classification accuracy. • Show the most effective model, the multilayer perceptron, and employ Focal Loss to handle imbalanced datasets and enhance classification. • Assess performance with robust metrics, including G-mean, Matthews Correlation Coefficient, and Kolmogorov-Smirnov statistics, ensuring reliable evaluation. • Demonstrate real-world applicability, supporting its potential use in clinical settings to improve diagnostic decision-making.

Source: Healthcare Analytics

DOI: 10.1016/j.health.2025.100388

Authors: Preeda Mengsiri, Ratchadaporn Ungcharoen, Sethavidh Gertphol

Institution: Kasetsart University

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,790