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Classification of pure conduct disorder from healthy controls based on indices of brain networks during resting state.
Medical & Biological Engineering & Computing ( IF 3.2 ) Pub Date : 2020-07-09 , DOI: 10.1007/s11517-020-02215-8
Jiang Zhang 1 , Yuyan Liu 1 , Ruisen Luo 1 , Zhengcong Du 2 , Fengmei Lu 3 , Zhen Yuan 4 , Jiansong Zhou 5 , Shasha Li 6, 7
Affiliation  

Conduct disorder (CD) is an important mental health problem in childhood and adolescence. There is presently a trend of revealing neural mechanisms using measures of brain networks. This study goes further by presenting a classification scheme to distinguish subjects with CD from typically developing healthy subjects based on measures of small-world networks. In this study, small-world networks were constructed, and feature data were generated for both the CD and healthy control (HC) groups. Two methods of feature selection, including the F-score and feature projection with singular value decomposition (SVD), were used to extract the feature data. Furthermore, and importantly, the classification performances were compared between the results from the two methods of feature selection. The selected feature data by SVD were employed to train three classifiers—least squares support vector machine (LS-SVM), naive Bayes and K-nearest neighbour (KNN)—for CD classification. Cross-validation results from 36 subjects showed that CD patients can be separated from HC with a sensitivity, specificity and overall accuracy of 88.89%, 100% and 94.44%, respectively, by using the LS-SVM classifier. These findings suggest that the combination of the LS-SVM classifier with SVD can achieve a higher degree of accuracy for CD diagnosis than the naive Bayes and KNN classifiers.

Graphical abstract



中文翻译:

来自健康对照者的纯行为障碍分类基于静止状态下脑网络的指标。

品行障碍(CD)是儿童和青少年时期重要的心理健康问题。目前存在使用脑网络的测量揭示神经机制的趋势。这项研究通过提出一种分类方案,根据小世界网络的测量结果,将分类为CD的受试者与通常发展为健康的受试者区分开来。在这项研究中,构建了小世界网络,并为CD和健康对照组(HC)组生成了特征数据。两种特征选择方法,包括F-score和具有奇异值分解(SVD)的特征投影用于提取特征数据。此外,重要的是,在两种特征选择方法的结果之间对分类性能进行了比较。通过SVD选择的特征数据被用于训练三个分类器-最小二乘支持向量机(LS-SVM),朴素贝叶斯和K最近邻(KNN)-用于CD分类。来自36名受试者的交叉验证结果表明,使用LS-SVM分类器可以将CD患者与HC分离,其敏感性,特异性和整体准确性分别为88.89%,100%和94.44%。这些发现表明,与朴素的贝叶斯和KNN分类器相比,LS-SVM分类器与SVD的结合可以实现更高的CD诊断准确性。

图形概要

更新日期:2020-07-10
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