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Prediction of Autism Spectrum Disorder Using Feature Engineering for Machine Learning Classifiers

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Intelligent Computing Paradigm and Cutting-edge Technologies (ICICCT 2020)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 21))

Abstract

Machine Learning presents a brand new method of predicting children with Autism Spectrum Disorder (ASD) in an early stage with different behavioral analytics. Predicting autistic’s characters through screening trials is very high-priced and long duration. According to the facts of WHO, the variety of patients identified with ASD is steadily growing. Such children are essentially not able to interact with others, put off with the acquisition of linguistic, Cognitive, repetitive behavioral, speech, and non-verbal communique. The goal of the paper is to awareness of the early deduction of ASD from the affected individual. Feature engineering is a process that extracts the appropriate features from the dataset for predictive modeling. In this study, features are analyzed and reduce in three different datasets of ASD with the categories of age. The reduced feature set is investigated with the machine learning classifiers such as SVM, RANDOM FOREST (RF), KNN. The overall performance of the prognostic models is classified in the frame of accuracy and sensitivity performance metrics. In precise, the RF method categorized the data with higher precision for ASD datasets.

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Authors and Affiliations

  1. S.D.N.B. Vaishnav College for Women, University of Madras, Chennai, Tamil Nadu, India

    N. Priya & C. Radhika

Authors
  1. N. Priya
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  2. C. Radhika
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Corresponding author

Correspondence to C. Radhika .

Editor information

Editors and Affiliations

  1. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  2. Department of Creative Technologies and Product Design, National Taipei University of Business, Taipei City, Taiwan

    Sheng-Lung Peng

  3. School of Engineering, RMIT University, Bundoora East Campus, Melbourne, VIC, Australia

    Milan Simic

  4. Department of Computer Information Systems, Al-Balqa’ Applied University, As-Salt, Jordan

    Basim Alhadidi

  5. Department of Computer Science and Engineering, Global Institute of Management and Technology, Kolkata, India

    Souvik Pal

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Priya, N., Radhika, C. (2021). Prediction of Autism Spectrum Disorder Using Feature Engineering for Machine Learning Classifiers. In: Favorskaya, M.N., Peng, SL., Simic, M., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2020. Learning and Analytics in Intelligent Systems, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-65407-8_5

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