A two-step prediction model using sleep-related features effectively predicted ischemic stroke events, achieving an Area Under the Curve of 0.919, Precision of 63%, and Recall of 85%.
Observational (n=179)
Can a prediction model using sleep-related features accurately predict ischemic stroke?
A novel two-step prediction model using sleep-related features demonstrated high accuracy (AUC 0.919) in predicting ischemic stroke.
Estimación del efecto: AUC 0.919
Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogram-related features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.
Xie et al. (Fri,) conducted a observational in Ischemic stroke (n=179). Two-step prediction model using sleep-related features was evaluated on Prediction of stroke events (AUC 0.919). A two-step prediction model using sleep-related features effectively predicted ischemic stroke events, achieving an Area Under the Curve of 0.919, Precision of 63%, and Recall of 85%.