The invention discloses a cerebrovascular
disease risk prediction method, and particularly relates to the field of
disease risk prediction, and the method comprises the following steps: S1, collecting basic clinical data, dynamic physiological parameter data and image
feature data of a target object, and constructing a
data set; s2, performing
feature extraction on the basic clinical data, the dynamic physiological parameter data and the image
feature data, and fusing the extracted features to obtain a fused
feature set; s3, constructing a fusion prediction model through the fusion
feature set; and S4, inputting the
data set into the fusion prediction model, and outputting a risk prediction result. According to the method, the information short board of a single
data type is made up, different types of data are converted into a unified computable form through standardized quantitative
processing, core information with risk indication significance is effectively screened out in combination with abnormal data proportion analysis and dynamic fluctuation intensity evaluation, and the recognition capability of early potential risks of diseases is remarkably improved.