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.

翻译：目的：可靠预测烟雾病（MMD）患者出血风险的工具有重要临床价值。本文旨在开发三种机器学习分类算法来预测烟雾病出血风险。方法：回顾性分析2009年至2015年间我院收治的连续MMD患者的临床资料。通过分析人口学、临床及影像学数据，构建人工神经网络（ANN）、支持向量机（SVM）和随机森林模型。结果：我们提取了33个参数作为模型输入，包括11个人口学特征和22个影像学特征。在所有分类结果比较中，ANN取得了最高总体准确率75.7%（95% CI，68.6%-82.8%），其次为SVM的69.2%（95% CI，56.9%-81.5%）和随机森林的70.0%（95% CI，57.0%-83.0%）。结论：所提出的ANN框架可基于临床信息和影像学特征，成为预测成年MMD患者出血风险的潜在有效工具。