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Early Diagnose of Autism Spectrum Disorder Using Machine Learning Based on Simple Upper Limb Movements

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Hybrid Intelligent Systems (HIS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 923))

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Abstract

The importance of early diagnosis of autism that leads to early intervention such thing shall increase the results of treating it. The Autism Spectrum Disorder (ASD) affects the children activities and caused difficulties in interaction, impairments in communication, delayed speech, and weak eye contact. These activities used as the base for ASD diagnosis decision. Children move their upper limb before some of the other activities. Moving upper limb can be based for ASD diagnosis decision for autistic children. Such paper examines diagnosing the ASD that depends on motioning the children’s upper-limb aged between two and four years based on executing specific procedures and machine learning. The approach that such study utilized is both (LDA) Linear Discriminant Analysis in order to elicit the features and (SVM) Support Vector Machines for classifying thirty children such study selected fifteen autistic children out of fifteen non-autistic children by testing the collected data that are collected from doing an easy task. The results of such study have accomplished an optimal sortation accuracy of 100% and the average accuracy of 93.8%. Such outcomes provide more proof of simple brachium motioning that might be utilized in sorting poor performance of autistic children precisely.

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Acknowledgments

Researchers acknowledge for Scientific Institute IRCCS “Eugenio Medea in Italy” for permitting the authors to access this data set.

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Authors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

    Mohammad Wedyan & Adel Al-Jumaily

  2. IRCCS Eugenio Medea, Scientific Institute, Bosisio Parini, Lecco, Italy

    Alessandro Crippa

Authors
  1. Mohammad Wedyan
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  2. Adel Al-Jumaily
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  3. Alessandro Crippa
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Corresponding author

Correspondence to Adel Al-Jumaily .

Editor information

Editors and Affiliations

  1. School of Engineering, Instituto Superior de Engenharia (ISEP/IPP), Porto, Portugal

    Ana Maria Madureira

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  3. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  4. Department of Production and Systems, University of Minho, GuimarĂŁes, Portugal

    Maria Leonilde Varela

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Wedyan, M., Al-Jumaily, A., Crippa, A. (2020). Early Diagnose of Autism Spectrum Disorder Using Machine Learning Based on Simple Upper Limb Movements. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_48

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