2019
DOI: 10.1007/s40489-019-00158-x
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Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review

Abstract: Autism spectrum disorder (ASD) research has yet to leverage Bbig data^on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in AS… Show more

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Cited by 157 publications
(77 citation statements)
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“…Machine learning is a promising tool for investigating the replicability of patterns across larger, more heterogeneous datasets [11][12][13]. For automated diagnosis of ASD, personal characteristic (PC) data, such as intelligence quotient (IQ) and Social Responsiveness Scale (SRS) score have been adopted in several studies [14][15][16].…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning is a promising tool for investigating the replicability of patterns across larger, more heterogeneous datasets [11][12][13]. For automated diagnosis of ASD, personal characteristic (PC) data, such as intelligence quotient (IQ) and Social Responsiveness Scale (SRS) score have been adopted in several studies [14][15][16].…”
Section: Introductionmentioning
confidence: 99%
“…Although the prognosis of autism may be carried out at any age however the signs and symptoms generally appear inside the first 24 months of life and develop thru time [17]. ASD youngsters are identified for their impairment in social interplay skills and deciphering the emotional facial features in other [18]. Autism Spectrum Condition (ASC) is a fixed of neuro development syndromes that affects mind functions [21].…”
Section: Introductionmentioning
confidence: 99%
“…LOGISTIC REGRESSION (LR): LR is a statistical linear model used for binary class, taking as input a set of independent attributes. The independent attributes can be discrete or non-stop and are used to predict the possibility of the goal result taking a suitable binary value [18].  K-NN: K-NN is a kind of non-parametric or instance-based learning in which the undertaking is simplest anticipated domestically and all computation is deferred till classification.…”
mentioning
confidence: 99%
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“…While review studies such as Hyde et al [24] and Thabtah [25] reported on ASD studies focusing on a single AI method, to our knowledge, no literature review has been conducted on the broad use of AI technology to distinguish individuals with ASD through an emphasis on behavioral aspects. Therefore, the aim of this study is to summarize findings on how AI can be implemented into the current evaluation process and explore other potential behavioral aspects that can be used to enhance efficiency in the detection of ASD.…”
Section: Introductionmentioning
confidence: 99%