Prognosticating global functional outcome in the recurrent ischemic stroke using baseline clinical and pre-clinical features: A machine learning study

Background and Purpose: Recurrent ischemic stroke (RIS) induces additional functional limitations in patients. Prognosticating globally functional outcome (GFO) in RIS patients is thereby important to plan a suitable rehabilitation programme. This study sought to investigate the ability of baseline features for classifying the patients with and without improving GFO (task 1) and identifying patients with poor GFO (task 2) at the third month after discharging from RIS. Methods: A total of 86 RIS patients were recruited and divided into the training set and testing set (50:50). The clinical and pre-clinical data were recorded. The outcome was the changes in Modified Rankin Scale (mRS) (task 1) and the mRS score at the third month (mRS 0-2: good GFO, mRS >2: poor GFO) (task 2). The permutation importance ranking method selected features. Four algorithms were trained on the training set with five-fold cross-validation. The best model was tested on the testing set. Results: In task 1, the support vector machine (SVM) model outperformed the other models, with the high performance matrix on the training set (sensitivity = 0.80; specificity = 1.00) and the testing set (sensitivity = 0.80; specificity = 0.95). In task 2, the SVM model with selected features also performed well on both datasets (training set: sensitivity = 0.76; specificity = 0.92; testing set: sensitivity = 0.72; specificity = 0.88). Conclusion: A machine learning model could be used to classify GFO responses to treatment and identify the third-month poor GFO in RIS patients, supporting physicians in clinical practice.