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A Comprehensive Analysis of Autism Spectrum Disorder Using Machine Learning Algorithms: Survey

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Power Engineering and Intelligent Systems (PEIS 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1097))

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Abstract

One of the psychological disorders known as autism spectrum disorders (ASD) is a very challenging one to analyze and goes undiagnosed in many people. This condition develops from birth and persists throughout life, and has no cure. It is possible to predict ASD using a variety of indicators, including functional magnetic resonance imaging (fMRI) data, kinematic traits, game-based applications, questionnaires given to parents and guardians, social reciprocity, head motion, motor activities, and eye-tracking. A better prognosis for the patient can be achieved with earlier prediction. This research work provides a thorough overview of the various machine learning and artificial intelligence algorithms utilized for ASD diagnosis and prediction in patients of various ages using clinical methods. This article also emphasizes the datasets that were utilized to predict autism in individuals, their results, limitations, and the hindrances of the methods involved.

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Authors and Affiliations

  1. Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641032, India

    D. Aarthi & S. Kannimuthu

Authors
  1. D. Aarthi
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  2. S. Kannimuthu
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Correspondence to D. Aarthi .

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Editors and Affiliations

  1. National Institute of Technology Delhi, New Delhi, Delhi, India

    Vivek Shrivastava

  2. South Asian University New Delhi, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India

    B. K. Panigrahi

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Aarthi, D., Kannimuthu, S. (2024). A Comprehensive Analysis of Autism Spectrum Disorder Using Machine Learning Algorithms: Survey. In: Shrivastava, V., Bansal, J.C., Panigrahi, B.K. (eds) Power Engineering and Intelligent Systems. PEIS 2023. Lecture Notes in Electrical Engineering, vol 1097. Springer, Singapore. https://doi.org/10.1007/978-981-99-7216-6_20

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  • DOI: https://doi.org/10.1007/978-981-99-7216-6_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7215-9

  • Online ISBN: 978-981-99-7216-6

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