Machine Learning Models of Hemorrhage/Ischemia in Moyamoya Disease and Analysis of Its Risk Factors

Object: Identify the risk factors for hemorrhage/ischemia in patients with moyamoya disease and establish models using Logistic regression (LR), XGboost and Multilayer Perceptron (MLP), evaluating and comparison the effects of those models; providing theoretical basis for moyamoya disease patients to prevent stroke recurrence. Methods: : This retrospective study used data from the database of Jiang Xi Province Medical Big Data Engineering & Technology Research Center; the data of patients with moyamoya disease admitted to the second affiliated hospital of Nanchang university from January 1, 2012 to December 31, 2019 were collected. A total of 994 patients with moyamoya disease were screened, including 496 patients with cerebral infarction and 498 patients with cerebral hemorrhage. LR, XGboost and MLP were used to establish models for hemorrhage /ischemia in moyamoya disease, the effects of different models were verified and compared. Result: LR, XGboost and MLP models all had good discrimination (AUC>0.75), and their AUC value are 0.9227（95%CI:0.9215-0.9239）、0.9677(95%CI:0.9657-0.9696)、 0.9672（95%CI：0.9643-0.9701）. Compared with LR model, the prediction ability of XGboost and MLP model in training and test set is improved, which is increased by 18.11% and 14.34% respectively in training set, and there is a significant difference. Conclusion: Compared with the traditional LR model, the machine learning models are more effective in predicting hemorrhage/ischemia in moyamoya disease.