A machine learning and neural network approach for classifying multidrug-resistant bacterial infections
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 he...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442525000073 |
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| author | Preeda Mengsiri Ratchadaporn Ungcharoen Sethavidh Gertphol |
| author_facet | Preeda Mengsiri Ratchadaporn Ungcharoen Sethavidh Gertphol |
| author_sort | Preeda Mengsiri |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-955ff6ed2ebe4daab7c084ad9b1598b7 |
| institution | Kabale University |
| issn | 2772-4425 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-955ff6ed2ebe4daab7c084ad9b1598b72025-08-20T03:31:06ZengElsevierHealthcare Analytics2772-44252025-06-01710038810.1016/j.health.2025.100388A machine learning and neural network approach for classifying multidrug-resistant bacterial infectionsPreeda Mengsiri0Ratchadaporn Ungcharoen1Sethavidh Gertphol2Biomedical Data Science Program, Faculty of Science, Kasetsart University, ThailandBiomedical Data Science Program, Faculty of Science, Kasetsart University, Thailand; Faculty of Public Health, Kasetsart University, Chalermphrakiat Sakon Nakhon Province Campus, Thailand; Corresponding author. Faculty of Public Health, Kasetsart University, Chalermphrakiat Sakon Nakhon Province Campus, Thailand.Biomedical Data Science Program, Faculty of Science, Kasetsart University, Thailand; Department of Computer Science, Faculty of Science, Kasetsart University, ThailandAntimicrobial 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.http://www.sciencedirect.com/science/article/pii/S2772442525000073Machine learningNeural networkElectronic health recordsMultidrug resistanceGram-negative bacteria |
| spellingShingle | Preeda Mengsiri Ratchadaporn Ungcharoen Sethavidh Gertphol A machine learning and neural network approach for classifying multidrug-resistant bacterial infections Healthcare Analytics Machine learning Neural network Electronic health records Multidrug resistance Gram-negative bacteria |
| title | A machine learning and neural network approach for classifying multidrug-resistant bacterial infections |
| title_full | A machine learning and neural network approach for classifying multidrug-resistant bacterial infections |
| title_fullStr | A machine learning and neural network approach for classifying multidrug-resistant bacterial infections |
| title_full_unstemmed | A machine learning and neural network approach for classifying multidrug-resistant bacterial infections |
| title_short | A machine learning and neural network approach for classifying multidrug-resistant bacterial infections |
| title_sort | machine learning and neural network approach for classifying multidrug resistant bacterial infections |
| topic | Machine learning Neural network Electronic health records Multidrug resistance Gram-negative bacteria |
| url | http://www.sciencedirect.com/science/article/pii/S2772442525000073 |
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