Machine Learning-Based Model for Prediction of Early Post-Stroke Fatigue in Patients With Stroke: A Longitudinal Study

Background Post-stroke fatigue, as one of the long-lasting physical and mental symptoms accompanying stroke survivors, will seriously affect the daily living ability and quality of life of stroke patients. Objective The aim of this study was to develop machine learning (ML) algorithms to predict early post-stroke fatigue among patients with stroke. Methods A longitudinal study of 702 patients with stroke followed for 3 months. Twenty-three clinical features were obtained from medical records and questionnaires before discharge. Early post-stroke fatigue was assessed using the Fatigue Severity Scale. The dataset was randomly divided into a training group (70%) and an internal validation group (30%), applied oversampling, 10-fold cross-validation, and grid search to optimize the hyperparameter. Feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Sixteen ML algorithms were performed to predict early post-stroke fatigue in this study. Accuracy, precision, recall, F 1 score, area under the receiver operating characteristic curve (AUC), and brier score were used to evaluate the models performance. Results Among the 16 ML algorithms, the Bagging model was the optimal model for predicting early post-stroke fatigue in patients with stroke (AUC = 0.8479, accuracy = 0.7518, precision = 0.5741, recall = 0.7209, F 1 score = 0.6392, brier score = 0.1490). The feature selection based on LASSO revealed that risk factors for early post-stroke fatigue in patients with stroke included anxiety, sleep, social support, family care, pain, depression, neural-functional defect, quit/no drinking, balance function, type of stroke, sex, heart disease, smoking, and hemiplegia. Conclusions In this study, the Bagging model proved to be effective in predicting early post-stroke fatigue.