  Objective: Reliable tools to predict moyamoya disease (MMD) patients at risk
for hemorrhage could have significant value. The aim of this paper is to
develop three machine learning classification algorithms to predict hemorrhage
in moyamoya disease. Methods: Clinical data of consecutive MMD patients who
were admitted to our hospital between 2009 and 2015 were reviewed.
Demographics, clinical, radiographic data were analyzed to develop artificial
neural network (ANN), support vector machine (SVM), and random forest models.
Results: We extracted 33 parameters, including 11 demographic and 22
radiographic features as input for model development. Of all compared
classification results, ANN achieved the highest overall accuracy of 75.7% (95%
CI, 68.6%-82.8%), followed by SVM with 69.2% (95% CI, 56.9%-81.5%) and random
forest with 70.0% (95% CI, 57.0%-83.0%). Conclusions: The proposed ANN
framework can be a potential effective tool to predict the possibility of
hemorrhage among adult MMD patients based on clinical information and
radiographic features.
