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Abstract：
The diagnosis based on clinical assessment of pediatric bipolar disorder (PBD) may sometimes lead to misdiagnosis in clinical practice. For the past several years, machine learning (ML) methods were introduced for the classification of bipolar disorder (BD), which were helpful in the diagnosis of BD. In this study, brain cortical thickness and subcortical volume of 33 PBD-I patients and 19 age-sex matched healthy controls (HCs) were extracted from the magnetic resonance imaging (MRI) data and set as features for classification. The dimensionality reduced feature subset, which was filtered by Lasso or f_classif, was sent to the six classifiers (logistic regression (LR), support vector machine (SVM), random forest classifier, naïve Bayes, k-nearest neighbor, and AdaBoost algorithm), and the classifiers were trained and tested. Among all the classifiers, the top two classifiers with the highest accuracy were LR (84.19%) and SVM (82.80%). Feature selection was performed in the six algorithms to obtain the most important variables including the right middle temporal gyrus and bilateral pallidum, which is consistent with structural and functional anomalous changes in these brain regions in PBD patients. These findings take the computer-aided diagnosis of BD a step forward.
摘要：
基于临床评估的儿童双相情感障碍（PBD）的诊断有时会导致临床实践中的误诊。在过去的几年里,引入机器学习(ML)方法对双相情感障碍(BD)进行分类，这对BD的诊断有帮助。在这项研究中,我们从磁共振成像(MRI)数据中提取了33例PBD-I患者和19例年龄-性别匹配的健康对照(HCs)的大脑皮层厚度和皮层下体积,并将其设置为分类特征.降维的特征子集，由Lasso或f_classif过滤，被发送到六个分类器(逻辑回归(LR)，支持向量机(SVM)，随机森林分类器,天真贝叶斯，k-最近邻,和AdaBoost算法），并对分类器进行了训练和测试。在所有分类器中，准确率最高的前两个分类器是LR(84.19%)和SVM(82.80%)。在六种算法中进行特征选择，以获得最重要的变量，包括右颞中回和双侧苍白球，这与PBD患者这些脑区的结构和功能异常变化一致。这些发现使BD的计算机辅助诊断向前迈进了一步。
