Journal of Computing Science and Engineering

  • 제10권4호
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  • Pages.111-117
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  • 2016
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  • 1976-4677(pISSN)
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  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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An ADHD Diagnostic Approach Based on Binary-Coded Genetic Algorithm and Extreme Learning Machine

  • Sachnev, Vasily (School of Information, Communication and Electronics Engineering, The Catholic University of Korea) ;
  • Suresh, Sundaram (School of Computer Engineering, Nanyang Technological University)
  • 투고 : 2016.11.01
  • 심사 : 2016.12.05
  • 발행 : 2016.12.30
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초록

An accurate approach for diagnosis of attention deficit hyperactivity disorder (ADHD) is presented in this paper. The presented technique efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and typically developing control (TDC) by using only structural magnetic resonance imaging (MRI). The research examines structural MRI of the hippocampus from the ADHD-200 database. Each available MRI has been processed by a region-of-interest (ROI) to build a set of features for further analysis. The presented ADHD diagnostic approach unifies feature selection and classification techniques. The feature selection technique based on the proposed binary-coded genetic algorithm searches for an optimal subset of features extracted from the hippocampus. The classification technique uses a chosen optimal subset of features for accurate classification of three subtypes of ADHD and TDC. In this study, the famous Extreme Learning Machine is used as a classification technique. Experimental results clearly indicate that the presented BCGA-ELM (binary-coded genetic algorithm coupled with Extreme Learning Machine) efficiently classifies TDC and three subtypes of ADHD and outperforms existing techniques.

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참고문헌

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