Abstract
Distinguishing Mycobacterium abscessus subspecies presents significant diagnostic challenges due to their genetic homogeneity and variability in analytical platforms. Our research combines matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry with machine learning (ML) approaches to enhance discrimination accuracy, utilizing 325 spectra profiles from diverse European hospitals. The analytical pipeline incorporates specialized techniques for geographical data harmonization, feature selection, and balancing class representation. The best model employs support vector machines (SVMs) with ComBat correction, Boruta feature selection, and centroid clustering for class imbalance, achieving a discrimination performance of 97% F1 score and 97.17% AUC-ROC on test samples. Noteworthily, most tested models improved their discrimination performance with the approach and demonstrated consistent performance metrics with high geometric mean (GEO) and index balanced accuracy (IBA) metrics (>0.90), ensuring consistent sensitivity and specificity across all subspecies. SHAP (SHapley Additive exPlanations) validated the biological relevance of selected spectral features, particularly improving discrimination of the diagnostically challenging M. abscessus subsp. bolletii. This work advances the state-of-the-art in M. abscessus classification, providing a scalable analytical framework for enhanced microbial diagnostics and targeted antimicrobial therapy selection.