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Attention Deficit Hyperactivity Disorder Using Machine Learning

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Evolution in Signal Processing and Telecommunication Networks (ICMEET 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1155))

  • 49 Accesses

Abstract

High temporal resolution is provided by EEG signals, which is helpful for evaluating and diagnosing youngsters that suffer with ADHD. The goal of this research is to produce a model for ML for identifying youngsters with ADHD and healthy controls 60 youngsters having ADHD and 60 healthy controls provided EEG readings for this investigation who were doing cognitive activities were collected from an open-access database. Three classifiers—AdaBoost, ANN, and RF—used to identify and further test the regional contributions to achieving improved accuracy. 19 channels of EEG data are utilized as input characteristics for classifiers, both individually and in combinatorial groupings. When every channel is considered and the total performance of all the classifiers is evaluated, the Random Forest has the greatest accuracy (80.48%). This study demonstrates the distinct physiological differences between youngsters with ADHD an acronym for attention deficit hyperactivity disorder children who are typically growing and developing present in their brain activity and may to make a diagnosis.

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Authors and Affiliations

  1. Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, AndhraPradesh, India

    Pravali Parvataneni, Suneetha Manne, Sandhyarani Chandaka & Sk. Affroz

Authors
  1. Pravali Parvataneni
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  2. Suneetha Manne
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  3. Sandhyarani Chandaka
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  4. Sk. Affroz
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Corresponding author

Correspondence to Suneetha Manne .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology, Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Department of Electronics and Communication Engineering, Raghu Institute of Technology, Visakhapatnam, Andhra Pradesh, India

    P. Satish Rama Chowdary

  3. Autonomous University of Baja California, Mexicali, Baja California, Mexico

    Wendy Flores-Fuentes

  4. Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

    Shabana Urooj

  5. Department of Electronics and Communication Engineering, National Institute of Technology Mizoram, Aizawl, India

    Rudra Sankar Dhar

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

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Parvataneni, P., Manne, S., Chandaka, S., Affroz, S. (2024). Attention Deficit Hyperactivity Disorder Using Machine Learning. In: Bhateja, V., Chowdary, P.S.R., Flores-Fuentes, W., Urooj, S., Sankar Dhar, R. (eds) Evolution in Signal Processing and Telecommunication Networks. ICMEET 2023. Lecture Notes in Electrical Engineering, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-97-0644-0_23

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  • DOI: https://doi.org/10.1007/978-981-97-0644-0_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0643-3

  • Online ISBN: 978-981-97-0644-0

  • eBook Packages: EngineeringEngineering (R0)

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