Predicting Bacterial Antibiotic Resistance using MALDI-TOF Mass Spectrometry Databases with ELM Applications

• Published in: Journal of computer science and technology (La Plata) Vol. 24; no. 2; p. e08
• Main Authors: Tirado, Felipe, Lopez Cortez, Xaviera, Macaya Mejías, Vicente, Zabala-Blanco, David, Manríquez-Troncoso, José M., Ahumada-García, Roberto
• Format: Journal Article
• Language: English
• Published: La Plata Universidad Nacional de la Plata, Journal of Computer Science and Technology 01-10-2024; Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
• Subjects: Algorithms; Antibiotic Resistance Prediction; Antibiotics; Drug resistance; E coli; Extreme Learning Machines; Klebsiella; Machine learning; Machine Learning in Medicine; MALDI-TOF Mass Spectrometry; Mass spectrometry; Neural networks; Oversampling; Weighted ELM
• ISSN: 1666-6046; 1666-6038
• DOI: 10.24215/16666038.24.e08

Early detection of antibiotic resistance is a crucial task, especially for vulnerable patients under prolonged treatments with a single antibiotic. To solve this, machine learning approaches have been reported in the state of art. Researchers have used MALDI-TOF MS in order to predict antibiotic resistance and/or susceptibility in bacterial samples. Weis, et al. implemented LR, LightGBM and ANN to study the antibiotic resistance on bacterial strains of Escherichia Coli, Staphylococcus Aureus, and Klebsiella Pneumoniae. Despite promising results, the models have not achieved perfect accuracy, specifically when the classes are unbalanced. On the other hand, Extreme Learning Machine (ELM) is a training algorithm for forward propagation of single hidden layer neural networks, which converges much faster than traditional methods and offers promising performance along with less programmer intervention. In this way, this study introduced improved ELMs, including two weighted ELMs proposed by Zong, and the SMOTE technique in order to create new synthetic samples of the minority class. After heuristic optimization of ELM hiper-parameters, results demonstrated 85% in accuracy and 85% in geometric mean for the classification problem in the case of weighted ELM 1 subject to the SMOTE technique of oversampling.