Machine learning-based screening tool for predicting the risk of oropharyngeal dysphagia in patients with ischemic stroke.

purposeDysphagia frequently complicates recovery after stroke, necessitating effective predictive tools. This study aimed to develop supervised machine learning models to predict dysphagia risk in ischemic stroke patients.

methodWe retrospectively analyzed data from 103 ischemic stroke patients, aged over 18 years, using fiberoptic endoscopic evaluation of swallowing (FEES) as the diagnostic standard for dysphagia. Clinical variables included age, sex, EAT-10 score, Functional Oral Intake Scale (FOIS

resultsPredictive models effectively distinguished dysphagia cases. Gradient Boosting and CatBoost achieved the highest AUC values (AUC = 0.99) on the test set. Logistic Regression performed consistently across test and cross-validation sets, achieving an AUC of 0.95 [95% CI: 0.86-1.00] and precision of 0.91 [95% CI: 0.80-1.00] in the test set, and high values in cross-validation (AUC = 0.93 [95% CI: 0.84-1.00]; precision = 0.92 [95% CI: 0.81-0.99]). Key predictors were FOIS

conclusionMachine Learning models showed promise for dysphagia screening post-stroke, with Gradient Boosting, CatBoost, and Logistic Regression showing strong clinical potential for decision support and early referral.