Accurate identification of neuropathies from electromyography (EMG) is crucial for automated diagnosis and future wearable screening systems. In this work, invasive EMG signals were processed to extract a 17 dimensional feature vector and classified as Healthy or Neuropathy using Light Gradient Boosting Machine (LightGBM). Model robustness was ensured through stratified 5 fold cross validation and repeated evaluation over 100 random dataset splits. SHapley Additive exPlanations (SHAP) were then applied to assess feature relevance and interpretability.

Based on the SHAP ranking, we introduced a SHAP-guided Iterative Feature Elimination (SHIFE) strategy, which removes features according to their estimated importance. This approach was compared with an unguided Iterative Feature Elimination (IFE) baseline that evaluates multiple feature combinations at each reduction step. Both methods improve performance with respect to the full feature set: IFE reduces the feature vector to 6 features and increases mean accuracy and AUC, while SHIFE reduces it to 7 features, preserving accuracy and improving AUC.