Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms - Wang, Peng; Cheng, Shuwen; Li, Yaxin; Liu, Li; Liu, Jia; Zhao, Qiang; Luo, Shuang (Frontiers in Public Health, 2022)
Background: Lumbar drainage is widely used in the clinic; however, forecasting lumbar drainage‑related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms. Methods: We utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN), were adopted for model training. Area under operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed. Results: In the training set, all the models had AUROC exceeded 0.8. SVM and RF models had AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, RF and AVM models maintained adequate AUROC (0.826 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease accuracy. Conclusions: RF and SVM models had the ability to predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.