Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms

Abstract

BackgroundLumbar 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.MethodsWe 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. The area under the 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.ResultsIn the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The 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, the RF and AVM models maintained adequate AUROC (0.828 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 in accuracy.ConclusionsThe RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

“…The ML algorithms used in the studies include logistic regression (LR) (25%), 46,48,51,54 multiple logistic regression (MLR) (25%), 50,52,53,61 support vector machine (SVM) (19%), 48,54,56 artificial neural network (ANN) (19%), 47,54,60 random forest (RF) (19%), 52,54,56 decision tree (DT) (12%), 57,58 nave‐Bayes (NB) (12%), 52,54 fast‐and‐frugal trees (FFTs) algorithm (6%), 56 and unsupervised ML approach (6%) 59 …”

“…The target meningitis diseases in the studies included bacterial meningitis (BM) (37%) 51–53,56,57,61 or enteroviral meningitis (EVM) (6%), 56 tuberculous meningitis (TBM) (25%), 48,54,55,61 viral meningitis (VM) (19%), 52,54,55 Neisseria meningitides (6%), 59 lumbar drainage‐related meningitis (LDRM) (6%), 47 healthcare‐associated ventriculitis and meningitis (HAVM) (6%), 58 cryptococcal meningitis (CM) (6%), 50 PM (6%), 46 and pediatric purulent meningitis (PPM) (6%) 49 …”

“…The majority of the included studies (56%) were carried out in low‐ and middle‐income countries (LMICs), according to the results of our systematic review 46–48,50,56,57,60,61 . This can become a strong point for improving primary care services and thus improving their economies, especially due to the unequal distribution of resources and health care provision in LMICs.…”

“…The ML algorithms used in the studies include logistic regression (LR) (25%), 46,48,51,54 multiple logistic regression (MLR) (25%), 50,52,53,61 support vector machine (SVM) (19%), 48,54,56 artificial neural network (ANN) (19%), 47,54,60 random forest (RF) (19%), 52,54,56 decision tree (DT) (12%), 57,58 nave-Bayes (NB) (12%), 52,54 fast-and-frugal trees (FFTs) algorithm (6%), 56 and unsupervised ML approach (6%). 59 The target meningitis diseases in the studies included bacterial meningitis (BM) (37%) [51][52][53]56,57,61 or enteroviral meningitis (EVM) (6%), 56 tuberculous meningitis (TBM) (25%), 48,54,55,61 viral meningitis (VM) (19%), 52,54,55 Neisseria meningitides (6%), 59 lumbar drainagerelated meningitis (LDRM) (6%), 47 healthcare-associated ventriculitis and meningitis (HAVM) (6%), 58 cryptococcal meningitis (CM) (6%), 50 PM (6%), 46 and pediatric purulent meningitis (PPM) (6%). 49…”

“…Ma et al 22 for detecting high-risk patients. 47 Drew also stated in a study that unsupervised ML provides a useful technique for detecting meningococcal vector risk factors. Additionally, it can be a helpful tool for sifting through big data sets to find groups of people who share similar traits and could enhance comprehension of patterns found in data sets.…”