Multi-class severity classification of peripheral nerve impairment using multimodal electrodiagnostic features and explainable machine learning.

OBJECTIVES: Peripheral nerve injuries impose substantial functional morbidity and long-term neurological impairment, yet electrodiagnostic severity assessment remains dependent on specialized expertise and susceptible to inter-examiner variability. This study proposes a supervised machine learning framework for automated four-class severity classification of upper-extremity peripheral nerve impairment using integrated electromyography (EMG) and nerve conduction study (NCS) features. METHODS: Five classifiers were evaluated using stratified five-fold cross-validation: support vector machine (SVM), random forest, LightGBM, XGBoost, and tabular prior-data fitted network (TabPFN). Feature selection combined Kruskal-Wallis and Chi-square statistical testing with Fisher score ranking. For the integrated EMG-NCS configuration, Gaussian Copula-based synthetic augmentation addressed the scarcity of paired electrodiagnostic samples (28 records). RESULTS: On the EMG dataset (n = 912), the ensemble of SVM, random forest, and TabPFN achieved the highest accuracy (73.77%) and macro-F1 (72.64%). On the NCS dataset (n = 1020), TabPFN achieved 98.04% accuracy and a macro-F1 of 98.60%, with the ensemble model producing comparable performance. On the augmented training distribution, SVM and TabPFN achieved 100% accuracy and macro-F1 on an independent held-out test set. Given the limited test size (n = 9), these findings are presented as proof of concept pending validation on larger paired clinical cohorts. SHAP-based explainability identified MUAP polyphasia percent as the primary EMG predictor, and sensory latency and motor conduction velocity as the key NCS predictors. DISCUSSION: These findings demonstrate the feasibility of interpretable machine learning for multimodal electrodiagnostic analysis and support its potential as a scalable decision-support tool for objective peripheral nerve impairment severity grading.