Objective To construct a prediction model for the recovery progress of swallowing function in stroke patients based on early rehabilitation data using the random forest algorithm, and validate its predictive performance. Methods This study is a prospective cohort study involving a total of 88 patients with acute stroke. Baseline information, neurological function scores, swallowing function assessments, rehabilitation training data, and physiological and biochemical indicators were collected for each patient. Machine learning algorithms, including Random Forest, Support Vector Machine (SVM), and Logistic Regression, were used to construct predictive models. Cross-validation was performed to evaluate the performance of each model in predicting rehabilitation outcomes, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Results The swallowing function score showed significant improvement at Week 12(with an average score increasing from 42.58 at enrollment to 68.47, P<0.001). Additionally, there were notable improvements in neurological function scores, as indicated by the modified Rankin Scale (mRS, P=0.021), Barthel Index (BI, P=0.008), and NIHSS (P=0.036). The Random Forest model exhibited the best performance in predicting swallowing function recovery, with an AUC of 0.809 in the test set. This significantly outperformed both the Support Vector Machine (AUC=0.774) and Logistic Regression (AUC=0.733, P<0.05), demonstrating its advantage in capturing complex nonlinear relationships within rehabilitation data. Conclusion The Random Forest model based on early rehabilitation data can effectively predict the progress of swallowing function recovery in stroke patients, providing a scientific basis for clinical formulation of personalized rehabilitation treatment plans.
Key words
stroke /
swallowing function /
recovery progression /
random forest /
prediction model
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