Machine Learning–Based Predictive Model for Post-Stroke Dementia

Abstract Backgound: Post-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Many factors may be related to PSD, including demographic, comorbidities, and examination characteristics. However, most existing methods are qualitative evaluations of independent factors, which ignore the interaction amongst various factors. Therefore, the purpose of this study is to explore the applicability of machine learning methods for predicting PSD. Methods: 9 acceptable features were screened out by the Spearman correlation analysis and Boruta algorithm. We developed and evaluated 8 machine learning (ML) models: logistic regression, elastic net, k-nearest neighbors, decision tree, extreme gradient boosting, support vector machine, random forest, and multilayer perceptron. Results: A total of 539 stroke patients were included in this study. Among the 8 models used to predict PSD, extreme gradient boosting and random forest showed the highest area under the curve (AUC), with values of 0.7287 and 0.7285, respectively. The most important features for predicting PSD included age, high sensitivity C-reactive protein, stroke side and location, and the occurrence of cerebral hemorrhage. Conclusion: Our findings suggest that ML models, especially extreme gradient boosting, can best predict the risk of PSD.