Predicting Mechanical Ventilation Requirement in Guillain-Barre Syndrome using a Multi-Functional Machine Learning Algorithm

Background: To develop and validate multiple Machine Learning (ML) algorithms that predict Mechanical Ventilation (MV) requirement in Guillain-Barre Syndrome (GBS), and to determine whether they outperform the additive, score-based prognostic models in current use. Methods: This retrospective study analysed 233 GBS patients (training set, n = 186; validation set, n = 47). Five algorithms (Deep Neural Network (DNN), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)) were trained and compared. Predictors were chosen by a three-method consensus pipeline executed inside each nested cross-validation fold, retaining 11 features. Whether BorderlineSMOTE was applied was determined per model by Optuna hyperparameter tuning. Hyperparameter tuning, probability calibration, and bootstrap resampling were applied; performance used accuracy, recall, F1, specificity, AUROC, and Brier score, with SHapley Additive exPlanations (SHAP) for model interpretability. Results: XGBoost achieved the strongest clinical performance (AUROC 0.807, accuracy 0.787, and recall 0.857), exceeding the validated EGRIS for MV (AUROC = 0.62). Calibration preserved recall (0.857) and shifted the operating point by one false positive while lowering the Brier score from 0.210 to 0.110 (naive Brier baseline 0.127, BSS = 0.134), so the deployed tool was developed using the probabilities from the calibrated XGBoost model. Consensus selection retained eleven predictors; blood prealbumin, blood FT3, and NLR ranked highest by both embedded importance and SHAP. The model was deployed as an interactive prognostic tool predicting MV risk at admission. Conclusions: ML algorithms substantially improve GBS prognosis by integrating eleven biomarker predictors, modelling nonlinear relationships, and providing SHAP-based interpretability. The single-centre sample is small, so external validation in larger, multi-centre cohorts is required before clinical deployment.