Abstract：
Autism is a neurodevelopmental disorder that manifests in individuals during childhood and has enduring consequences for their social interactions and communication. The prediction of Autism Spectrum Disorder (ASD) in individuals based on the differences in brain networks and activities have been studied extensively in the recent past, however, with lower accuracies. Therefore in this research, identification at the early stage through computer-aided algorithms to differentiate between ASD and TD patients is proposed. In order to identify features, a Multi-Layer Perceptron (MLP) model is developed which utilizes logistic regression on characteristics extracted from connectivity matrices of subjects derived from fMRI images. The features that significantly contribute to the classification of individuals as having Autism Spectrum Disorder (ASD) or typically developing (TD) are identified by the logistic regression model. To enhance emphasis on essential attributes, an AND operation is integrated. This involves selecting features demonstrating statistical significance across diverse logistic regression analyses conducted on various random distributions. The iterative approach contributes to a comprehensive understanding of relevant features for accurate classification. By implementing this methodology, the estimation of feature importance became more dependable, and the potential for overfitting is moderated through the evaluation of model performance on various subsets of data. It is observed from the experimentation that the highly correlated Left Lateral Occipital Cortex and Right Lateral Occipital Cortex ROIs are only found in ASD. Also, it is noticed that the highly correlated Left Cerebellum Tonsil and Right Cerebellum Tonsil are only found in TD participants. Among the MLP classifier, a recall of 82.61 % is achieved followed by Logistic Regression with an accuracy of 72.46 %. MLP also stands out with a commendable accuracy of 83.57 % and AUC of 0.978.
摘要：
自闭症是一种神经发育障碍，在儿童时期表现在个人身上，对他们的社交互动和交流具有持久的影响。近年来，基于大脑网络和活动差异的个体自闭症谱系障碍（ASD）的预测已经得到了广泛的研究，然而,精度较低。因此，在这项研究中，提出了通过计算机辅助算法在早期阶段进行识别，以区分ASD和TD患者。为了识别特征，开发了多层感知器（MLP）模型，该模型利用逻辑回归对从fMRI图像得出的受试者的连通性矩阵中提取的特征进行分析。通过逻辑回归模型确定了对患有自闭症谱系障碍（ASD）或典型发展（TD）的个体分类有重要贡献的特征。加强对本质属性的重视,一个AND操作是集成的。这涉及选择在对各种随机分布进行的各种逻辑回归分析中证明统计显著性的特征。迭代方法有助于全面理解相关特征以进行准确分类。通过实施这种方法，特征重要性的估计变得更加可靠，并且通过对各种数据子集的模型性能评估来缓和过度拟合的可能性。从实验中观察到，仅在ASD中发现高度相关的左枕骨外侧皮层和右枕骨外侧皮层ROI。此外，注意到高度相关的左小脑扁桃体和右小脑扁桃体仅在TD参与者中发现。在MLP分类器中，召回率达到82.61%，其次是Logistic回归，准确率为72.46%。MLP也以83.57%的准确度和0.978的AUC脱颖而出。
