Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data - PubMed
5 hours ago
- #machine learning
- #stroke
- #cognitive frailty
- Study developed ML models to predict long-term cognitive frailty risk in stroke patients using CHARLS data.
- XGBoost and Random Forest models showed highest performance (AUC 0.810 and 0.795).
- Key predictors identified: age, education, nutritional status, physical exercise, and IADL.
- SHAP values highlighted age and education as most significant factors.
- Model aims for early screening in primary care and targeted interventions.