The invention relates to a
stroke prognosis prediction model training method based on
quantum computing and
artificial intelligence, and relates to the technical field of prognosis effect prediction. According to the method, leukocyte
community parameters are introduced into an
acute ischemic stroke prognosis model for the first time, morphological function characteristics of leukocytes are detected through a
flow cytometry, and compared with traditional
inflammation indexes, the leukocyte prognosis model has higher sensitivity and objectivity and becomes an important index for predicting
neurological function impairment; the indexes closely related to cerebral apoplexy prognosis are screened out through
quantum calculation for the first time, and a new thought is provided for high-dimensional data
feature screening; meanwhile, by comparing 14 traditional
machine learning, integrated learning and
deep learning models, it is judged that the LightGBM model is an
acute ischemic stroke prognosis prediction model, and compared with the prior art, the LightGBM model is remarkably improved.