Machine Learning Classifiers for Autism Spectrum Disorder: A Review

Autism Spectrum Disorder (ASD) is a brain development disorder affects the ability to communicate and interact socially. There have been many studies using machine learning methods to classify autism including support vector machines, decision trees, naïve Bayes, random forests, logistic regression, K-nearest Neighbors and others. In this study provides a review of autism spectrum disorder using the machine learning algorithm is supervised learning. The initial study of the article was collected from website provided articles were in according to this study. The articles were collected from online databases and 16 research articles met the requirements in this study. Based on the results obtained, the most widely used algorithm in the literature study in this study is support vector machine (SVM) of 6S.75%. With the application of machine learning in the case of ASD expected to be able to accelerate and improve accuracy in determining a diagnosis.

[1]  Paolo Perego,et al.  Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities , 2015, Journal of autism and developmental disorders.

[2]  Sebastien Levy,et al.  Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism , 2017, Molecular Autism.

[3]  Yang Wang,et al.  Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster , 2018, Front. Genet..

[4]  Peter Washington,et al.  Mobile detection of autism through machine learning on home video: A development and prospective validation study , 2018, PLoS medicine.

[5]  Young Shin Kim,et al.  A systematic review of screening tools in non-young children and adults for autism spectrum disorder. , 2018, Research in developmental disabilities.

[6]  Yiheng Tu,et al.  Applying Eye Tracking to Identify Autism Spectrum Disorder in Children , 2018, Journal of autism and developmental disorders.

[7]  C. Kan,et al.  Predictive validity of self-report questionnaires in the assessment of autism spectrum disorders in adults , 2015, Autism : the international journal of research and practice.

[8]  Murray H Brilliant,et al.  Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder , 2018, Autism research : official journal of the International Society for Autism Research.

[9]  D. Wall,et al.  Use of machine learning for behavioral distinction of autism and ADHD , 2016, Translational Psychiatry.

[10]  Neda Abdelhamid,et al.  A machine learning autism classification based on logistic regression analysis , 2019, Health Information Science and Systems.

[11]  Kim Van Naarden Braun,et al.  Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder , 2016, PloS one.

[12]  Herschel Rabitz,et al.  High efficiency classification of children with autism spectrum disorder , 2018, PloS one.

[13]  D. Wall,et al.  Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning , 2015, Translational Psychiatry.

[14]  Zdenko Kovacic,et al.  Four tasks of a robot-assisted autism spectrum disorder diagnostic protocol: First clinical tests , 2014, IEEE Global Humanitarian Technology Conference (GHTC 2014).

[15]  Patricia A Patrick,et al.  Autism Spectrum Disorder Screening Instruments for Very Young Children: A Systematic Review , 2016, Autism research and treatment.

[16]  Weifeng Li,et al.  Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery , 2014, Remote. Sens..

[17]  F. Thabtah.  Machine learning in autistic spectrum disorder behavioral research: A review and ways forward , 2019, Informatics for health & social care.

[18]  Koray Karabekiroglu,et al.  Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders , 2018, Psychiatry and Clinical Psychopharmacology.

[19]  Dong Hoon Oh,et al.  Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning , 2017, European Neuropsychopharmacology.

[20]  Erik Linstead,et al.  Predicting Employer Recruitment of Individuals with Autism Spectrum Disorders with Decision Trees , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[21]  Matthew S. Goodwin,et al.  Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion. , 2016, Journal of child psychology and psychiatry, and allied disciplines.

[22]  Peter Washington,et al.  Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study , 2019, Journal of medical Internet research.

[23]  Baihua Li,et al.  Applying machine learning to identify autistic adults using imitation: An exploratory study , 2017, PloS one.

[24]  Alessandra Retico,et al.  The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study , 2016, Molecular Autism.

[25]  Li Yi,et al.  Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework , 2016, Autism research : official journal of the International Society for Autism Research.