Machine learning-based predictive model for post-stroke dementia.

ABSTRACT:

Background

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 (ML) methods for predicting PSD.

Methods

9 acceptable features were screened out by the Spearman correlation analysis and Boruta algorithm. We developed and evaluated 8 ML models: logistic regression, elastic net, k-nearest neighbors, decision tree, extreme gradient boosting, support vector machine, rand

SUBMITTER: Wei Z 

PROVIDER: S-EPMC11555950 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature