Detection of ADHD using Machine Learning Algorithms
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

Research Paper | Information Technology | India | Volume 8 Issue 1, January 2019

Detection of ADHD using Machine Learning Algorithms

Rohit Kale

Attention deficit hyperactivity disorder (ADHD) is a standout amongst the most widely recognized disorder in school-age kids. To date, the determination of ADHD is for the most part subjective and investigations of target symptomatic strategy are of awesome significance. Although numerous endeavors have been made as of late to explore the use of structural and functional images of brain, for diagnosis, out of which a few are relevant for ADHD. Since the available dataset is unlabeled, we present a programmed grouping structure (k-means) to create clusters in view of MRI image of ADHD patients and normal subjects and present in detail highlight determination, and classifier preparing techniques. Classification algorithm K-Nearest Neighbor creates a classifier which classifies the input into the respective category.

Keywords: ADHD, s-MRI, f-MRI, Machine Learning, k-means, anamoly, random forest

Edition: Volume 8 Issue 1, January 2019

Pages: 1055 - 1058

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How to Cite this Article?

Rohit Kale, "Detection of ADHD using Machine Learning Algorithms", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20194447, Volume 8 Issue 1, January 2019, 1055 - 1058

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