当前位置: X-MOL 学术Concurr. Comput. Pract. Exp. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Autism spectrum disorder detection using sequential minimal optimization-support vector machine hybrid classifier according to history of jaundice and family autism in children
Concurrency and Computation: Practice and Experience ( IF 2 ) Pub Date : 2021-07-14 , DOI: 10.1002/cpe.6498
Şule Yücelbaş 1 , Cüneyt Yücelbaş 1
Affiliation  

Autism spectrum disorder (ASD) is a neurodevelopmental disorder caused by several nervous system problems that change the function of the brain. It is either congenital or occurs in the early years of life. Although the cause of ASD is not known exactly, it is generally thought to be genetic. Today, the relationship of ASD with other diseases is under investigation. One of these diseases is jaundice. It has been shown in different medical studies that the probability of children, who were jaundiced in the newborn period and/or had a family history of autism, having this disorder is higher than the others. However, studies in this area using artificial intelligence techniques are limited. For this reason, in this study, the relationship between ASD with jaundice and/or family history of autism in children was emphasized by using current machine learning techniques and analyses. Datasets were established by digitizing verbal data obtained from children between the ages of 4 and 11: (1) subjects with a family history of autism, (2) subjects with a history of jaundice, (3) subjects with both history of jaundice and family autism, and (4) subjects with no conditions. Since the verbal datasets created by the answers from the children (with or without ASD) or the people around them were converted into numerical form with 0–1 coding, mathematical operations could be performed using these datasets. The four subgroups of data mentioned above were given to the sequential minimal optimization-support vector machine hybrid classifier after they were separated into training and test data via the stratified cross-validation method. The results were analyzed with various statistical parameters. In addition, the sequential forward floating selection algorithm was used to determine which features were not effective for ASD detection. Obtained results from all datasets can provide a new perspective for the literature. As a result, it was determined with a 100% success rate for dataset1 and dataset3. In addition, the ASD detection rate was calculated at 95.52% in children with a history of jaundice. Finally, considerable and meaningful interpretations were made about which features according to the history of jaundice and family autism for each dataset are more effective in ASD detection in children.

中文翻译:

根据儿童黄疸病史和家庭孤独症病史使用序列最小优化-支持向量机混合分类器检测孤独症谱系障碍

自闭症谱系障碍 (ASD) 是一种神经发育障碍,由多种神经系统问题引起,这些问题会改变大脑的功能。它要么是先天性的,要么发生在生命的早期。尽管 ASD 的病因尚不清楚,但通常认为与遗传有关。今天,ASD 与其他疾病的关系正在调查中。这些疾病之一是黄疸。不同的医学研究表明,在新生儿时期出现黄疸和/或有自闭症家族史的儿童患这种疾病的可能性高于其他儿童。然而,使用人工智能技术在这方面的研究是有限的。为此,在本研究中,通过使用当前的机器学习技术和分析,强调了 ASD 与黄疸和/或儿童自闭症家族史之间的关系。数据集是通过数字化从 4 至 11 岁儿童获得的口头数据建立的:(1) 有自闭症家族史的受试者,(2) 有黄疸病史的受试者,(3) 有黄疸病史和家族史的受试者自闭症,以及(4)没有条件的受试者。由于由儿童(有或没有 ASD)或他们周围的人的答案创建的口头数据集被转换为 0-1 编码的数字形式,因此可以使用这些数据集进行数学运算。将上述四个子组的数据通过分层交叉验证的方法分成训练数据和测试数据后,交给序列最小优化-支持向量机混合分类器。用各种统计参数分析结果。此外,使用顺序前向浮动选择算法来确定哪些特征对 ASD 检测无效。从所有数据集获得的结果可以为文献提供一个新的视角。结果,数据集1和数据集3的成功率确定为100%。此外,有黄疸病史的儿童 ASD 检出率为 95.52%。最后,
更新日期:2021-07-14
down
wechat
bug