文章基本信息

标题：Using Machine Learning Methods to Predict Autism Syndrome
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作者：Hosam Alhakami ; Fatimah Alajlani ; Alshymaa Alghamdi 等
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2020
卷号：20
期号：4
页码：221-228
出版社：International Journal of Computer Science and Network Security
摘要：Autism Spectrum Disorder (ASD) is the most common developmental disability affecting people globally. Around 100,000 people had been affected globally from the 1980s to 2016. ASDs are characterized by poor social skills, poor intelligence, poor verbal and nonverbal communication skills. In some situations, these effects can be far-reaching making parents overly stressed. The financial risks brought by ASDs are a major source of stress for parents. Also, parents get stress resulting from stigmatization from people in society who has little or no information about ASDs. This results in psychological stress among parents who persistently do not look for other support. It becomes worse if parents lack any support systems to give them encouragement. However, parents can get support from focus groups and homebased psychological help with their mental health practitioners. This study has shown the far-reaching consequences of parental stress as it inflicts relationships and child and family care. Moreover, it suggested an adaptive learning system to help parents choose the best learning environment for their autistic children and multiple algorithms were chosen from machine learning to compare between them, and the best algorithm that can predict autism was identified.
关键词：Data Analytics; Autism Spectrum Disorder; Machine Learning