Abstract
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.
Abstract (translated)
Objective: 可靠工具预测moyamoya disease (MMD)有出血风险的患者可能具有重要价值。本文的目标是开发三个机器学习分类算法来预测moyamoya disease的出血。方法:我们对我们在2009年至2015年期间收治的连续MMD患者的临床数据进行了回顾。年龄、临床和X线数据进行分析,以开发人工神经网络(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,包括11个年龄和22个X线特征的输入,用于模型开发。在所有比较的分类结果中,ANN取得了最高的整体准确性75.7%(95% CI,68.6%-82.8%),其次是SVM,准确性为69.2%(95% CI,56.9%-81.5%)和随机森林,准确性为70.0%(95% CI,57.0%-83.0%)。结论:提出的ANN框架可以基于临床信息和X线特征预测成人MMD患者的出血可能性。
URL
https://arxiv.org/abs/2302.00188