Meeting Banner
Abstract #4137

Integrating clinical and imaging features to predict recurrence of cerebrovascular events —— A machine learning study

mengting wei1, jinhao lv2, liuxian wang2, senhao zhang2, dongshan han2, xinrui wang2, and xin lou2
1Chinese PLA General Hospital, BeiJing, China, 2Chinese PLA General Hospital, beijing, China

Stroke is characterized by a high recurrence rate, and intervention after early identification of patients at risk of recurrence may improve their prognosis. After strict screening, 55 patients were enrolled in this study. the results show that it is feasible to identify patients with recurrent cerebrovascular events within one year by integrating clinical and imaging features through machine learning. RandomForest and NaiveBayes are the optimal algorithms, and HCR(hypoperfusion cubage ratio)can significantly optimize the recognition of these patients.

This abstract and the presentation materials are available to members only; a login is required.

Join Here