Interpretable Machine Learning Model for Predicting the Prognosis of Guillain-Barré Syndrome Patients

Machine learning (ML) is increasingly used in medical predictive modeling, but there are no studies applying ML to predict prognosis in Guillain-Barré syndrome (GBS).

The medical records of 223 patients with GBS were analyzed to construct predictive models that affect patient prognosis. Least Absolute Shrinkage and Selection Operator (LASSO) was used to filter the variables. Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), k-nearest Neighbour (KNN), Naive Bayes (NB), Neural Network (NN). Light Gradient Boosting Machine (LGBM) and Logistic Regression (LR) were used to construct predictive models. Clinical data from 55 GBS patients were used to validate the model. SHapley additive explanation (SHAP) analysis was used to explain the model. Single sample gene set enrichment analysis (ssGSEA) was used for immune cell infiltration analysis.

The AUCs (area under the curves) of the 8 ML algorithms including DT, RF, XGBoost, KNN, NB, NN, LGBM and LR were as follows: 0.75, 0.896 0.874, 0.666, 0.742, 0.765, 0.869 and 0.744. The accuracy of XGBoost (0.852) was the highest, followed by LGBM (0.803) and RF (0.758), with F1 index of 0.832, 0.794, and 0.667, respectively. The results of the validation set data analysis showed AUCs of 0.839, 0.919, and 0.733 for RF, XGBoost, and LGBM, respectively. SHAP analysis showed that the SHAP values of blood neutrophil/lymphocyte ratio (NLR), age, mechanical ventilation, hyporeflexia and abnormal glossopharyngeal vagus nerve were 0.821, 0.645, 0.517, 0.401 and 0.109, respectively.

The combination of NLR, age, mechanical ventilation, hyporeflexia and abnormal glossopharyngeal vagus used to predict short-term prognosis in patients with GBS has a good predictive value.