摘 要：目前在针对自闭症的分类研究中，构建阈值连接网络时，大多因不合理的阈值设置而影响最终分类结果。为了避免传统方法阈值选择的问题，文章对自闭症患者的最小生成树脑功能网络进行了研究，报告了正常被试与自闭症患者脑功能连接网络的差异，并设计了基于支持向量机的分类模型，实现对自闭症的分类工作。文中获得的分类准确率达到 81.76%，相比于传统方法具有更高的敏感度和特异性。
关键词：最小生成树；自闭症；支持向量机；辅助诊断
DOI:10.19850/j.cnki.2096-4706.2021.19.020
基金项目：西南民族大学中央高校基本 科研业务费专项资金优秀学生培养工程项目 （2020YYXS60）
中图分类号：TM18 文献标识码：A 文章编号：2096-4706（2021）19-0082-03
Research on Auxiliary Diagnosis of Autism Based on Minimum Spanning Tree
ZHAO Xiaoqi, ZHAO Liping, ZHU Cheng, TAN Ying
(School of Computer Science and Engineering, Southwest Minzu University, Chengdu 610041, China)
Abstract: In current classification studies for autism, the final classification results are mostly affected by unreasonable threshold settings when constructing threshold connectivity networks. In order to avoid the problem of threshold selection by traditional methods, the minimum spanning tree brain function network of autistic patients is studied in this paper, and the differences between the brain function connectivity network of normal subjects and autistic patients are reported, and a support vector machine-based classification model is designed to realize the classification work for autism. The classification accuracy obtained in this paper reaches 81.76%, which has higher sensitivity and specificity compared to traditional methods.
Keywords: minimum spanning tree; autism; support vector machine; auxiliary diagnosis
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作者简介：赵晓琦（1997.04—），女，锡伯族，辽宁铁岭人，硕士研究生在读，研究方向：智能信息处理。