UNASSIGNED: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia.
UNASSIGNED: By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models.
UNASSIGNED: The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC).
UNASSIGNED: The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients.
■为了提高脑部疾病的预测准确性,本研究调查了与精神分裂症相关的连接不良子网络和图结构。
■通过使用提出的结构连通性-深层图神经网络(sc-DGNN)模型,并与机器学习(ML)和深度学习(DL)模型进行比较。这项工作试图集中在88个受试者的扩散磁共振成像(dMRI),三个经典ML,和五个DL模型。
■提出了结构连通性深度图神经网络(sc-DGNN)模型,以有效地预测与精神分裂症相关的连通性异常,并且与传统的ML和DL(GNN)方法相比,在准确性方面表现出卓越的性能,灵敏度,特异性,精度,F1分数,和接收机工作特性下面积(AUC)。
■使用结构连接矩阵和实验结果表明,线性判别分析(LDA)在ML模型中的准确率为72%,sc-DGNN在DL模型中以93%的准确率进行区分精神分裂症和健康患者。