Prediction model for frailty risk in ischemic stroke patients: Application and validation of support vector machines...

This study aimed to develop a prediction model based on nomograms and support vector machines (SVM) to assess frailty risk in ischemic stroke patients. Clinical information of ischemic stroke patients admitted to our hospital from January 2023 to December 2024 was retrospectively collected. First, independent risk factors associated with frailty in ischemic stroke patients were identified through univariate and multivariate logistic regression analyses. Subsequently, a nomogram was constructed based on regression analysis results and validated using 10-fold cross-validation. Data were divided into training (70%) and validation (30%) sets. A SVM predictive model was constructed on the training set. The predictive performance of both models was compared using receiver operating characteristic curves. This study included 867 ischemic stroke patients, among whom 296 (34.14%) were classified as frail. The entire cohort was randomly divided into a training set (n = 607) and a validation set (n = 260) at a 7:3 ratio. Logistic regression analysis identified hypertension, impaired self-care ability, physical inactivity, reduced prognosis nutrition index, and depressive status as independent risk factors for frailty in ischemic stroke patients (P < .05). The nomogram demonstrated an area under the receiver operating characteristic curve (AUC) of 0.814 for frailty prediction, while the SVM model achieved an AUC of 0.842, indicating superior predictive capability. Further comparison revealed that while the nomogram offers greater intuitiveness and operational convenience in clinical practice, the SVM model demonstrated superior predictive accuracy. Hypertension, impaired self-care ability, physical inactivity, reduced prognosis nutrition index, and depressive states are independent risk factors for frailty in ischemic stroke patients. Both predictive models (nomogram and SVM) demonstrated high predictive accuracy; however, the SVM model outperformed the nomogram in predictive capability, while the latter retains unique advantages due to its simplicity and clinical applicability.