Machine learning-based prediction of fall within 6 months of stroke onset: A prospective study.

OBJECTIVE

To develop and evaluate machine learning-based models for predicting fall risk within 6 months of stroke onset.

METHODS

This prospective study enrolled acute stroke patients from three tertiary hospitals in Gansu Province, China. The study was conducted from December 2022 to October 2024. Participants were followed for 6 months. Two machine learning algorithms, decision tree (DT) and logistic regression (LR), were developed. Baseline data were collected within 72 h of admission. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), Brier score, accuracy, sensitivity, specificity, precision, F1-score.

RESULTS

Of the 388 patients in the final analytic cohort, 286 (73.7%) were male and 102 (26.3%) were female, with a mean age of 64.27 ± 11.30 years. The incidence of falls within 6 months after stroke was 28.1% (109/388). The LR model achieved an AUC of 0.859, accuracy of 79.3%, sensitivity of 0.547, specificity of 0.890, precision of 0.663, F1-score of 0.599, and Brier score of 0.133. The DT model achieved an AUC of 0.863, accuracy of 81.4%, sensitivity of 0.618, specificity of 0.892, precision of 0.692, F1-score of 0.652, and Brier score of 0.132. The LR model was visualized as a nomogram.

CONCLUSIONS

The LR and DT models showed comparable predictive performance for identifying patients at risk of falls within 6 months after stroke onset. These interpretable models may support early risk stratification. However, external validation is required to confirm their generalizability.