Attention Deficit Hyperactivity Disorder (Adhd) Detection Methods
M. Sheriff1, R. Gayathri2
1M. Sheriff, Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech, Chennai (Tamil Nadu), India.
2R. Gayathri, Research Supervisor, Associate Professor, Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Chennai (Tamil Nadu), India.
Manuscript received on 16 July 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 29 August 2019 | PP: 242-244 | Volume-8 Issue-2S5 July 2019 | Retrieval Number: B10500682S519/2019©BEIESP | DOI: 10.35940/ijrte.B1050.0782S519
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental-health disorders, affecting around 5%-10% of school-age children. This paper details about various methodologies for detecting and diagnosing the ADHD disease in patients using different soft computing and deep learning techniques. The limitations of advantages of each ADHD method were discussed in detail with its corresponding simulation results. The feature extraction method and its training with classification procedure for each conventional ADHD method were illustrated in detail.
Keywords: ADHD, Disorder, Features, Classifications, Diagnosing.
Scope of the Article: Probabilistic Models and Methods