文章基本信息

标题：Autism Spectrum Disorder Diagnosis using Optimal Machine Learning Methods
其他标题：Autism Spectrum Disorder Diagnosis using Optimal Machine Learning Methods
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作者：Maitha Rashid Alteneiji ; Layla Mohammed Alqaydi ; Muhammad Usman Tariq 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：9
DOI：10.14569/IJACSA.2020.0110929
出版社：Science and Information Society (SAI)
摘要：Autism spectrum disorder (ASD) is the disorder of communication and behavior that affects children and adults. It can be diagnosed at any stage of life. Most importantly, the first two years of life, regardless of ethnicity, race, or economic groups. There are different variations of ASD according to the severity and type of symptoms experienced by people. It is a lifelong disorder, but treatment and services can improve the symptoms. The literature focuses on one of the main methods used by physicians to diagnose ASD. Many types of research and medical reports have been reviewed; however, a few of them only give good medical results for the strong differentiation of ASD from healthy people. This paper focuses on using machine learning algorithms to predict an individual with specific ASD symptoms. The target is to predict an individual with specific ASD symptoms and finding the best machine learning model for diagnosis. Further, the paper aims to make the autism diagnosis faster to deliver the required treatment at an early stage of child development.
关键词：Autism diagnosis; autism disorder; autism detection; machine learning; ASD