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ICCCE 2021 pp 427–440Cite as

Effective Classification of Autism Spectrum Disorder Using Adaptive Support Vector Machine

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 828))

Abstract

Medical science today produces a great amount of data. Medical field is loaded with rich set of data with evidence and can be helpful in making decision. Autism Spectrum Disorder (ASD) is a category of neurodevelopmental diseases that cannot be cured but are mitigated by early diagnosis and intervention. Early diagnosis and prevention is more critical than cure for people with autism. ASD is present not only with children but also with adults and adolescents. Traditional classification algorithms attempt to give its best performance only with certain dataset that are related to certain diseases. Only very few algorithms available for the prediction of ASD, but it is for predicting ASD among children. Still now there exist no standard classification algorithm for the prediction of ASD among children, adults and adolescents. This research work attempts to find a solution to addressed problem by proposing Adaptive Support Vector Machine (ASVM) algorithm. ASVM is a modified version of SVM algorithm that meets the prediction of ASD more accurately. Tuning method is utilized to enhance the accuracy. To analyze the effective performance of ASVM against previous algorithms it has been tested with three different ASD screening datasets available for adults, children and adolescents. Results are measured using benchmark data mining metrics and it has been found that ASVM has better performance in classifying and predicting ASD in all considered datasets.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Nehru College of Management, Coimbatore, India

    Bindu George

  2. Department of Computing (AIML), Coimbatore Institute of Technology, Coimbatore, India

    E. Chandra Blessie

Authors
  1. Bindu George
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  2. E. Chandra Blessie
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Editor information

Editors and Affiliations

  1. BioAxis DNA Research Centre (P) Ltd., Hyderabad, India

    Amit Kumar

  2. Dynexsys, Sydney, NSW, Australia

    Stefan Mozar

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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George, B., Chandra Blessie, E. (2022). Effective Classification of Autism Spectrum Disorder Using Adaptive Support Vector Machine. In: Kumar, A., Mozar, S. (eds) ICCCE 2021. Lecture Notes in Electrical Engineering, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-16-7985-8_44

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  • DOI: https://doi.org/10.1007/978-981-16-7985-8_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7984-1

  • Online ISBN: 978-981-16-7985-8

  • eBook Packages: EngineeringEngineering (R0)

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