Applied Machine Learning Method to Predict Children With ADHD Using Prefrontal Cortex Activity: A Multicenter Study in Japan

Akira Yasumura*, Mikimasa Omori, Ayako Fukuda, Junichi Takahashi, Yukiko Yasumura, Eiji Nakagawa, Toshihide Koike, Yushiro Yamashita, Tasuku Miyajima, Tatsuya Koeda, Masao Aihara, Hisateru Tachimori, Masumi Inagaki

*この研究の対応する著者

研究成果: Article査読

7 被引用数 (Scopus)

抄録

Objective: To establish valid, objective biomarkers for ADHD using machine learning. Method: Machine learning was used to predict disorder severity from new brain function data, using a support vector machine (SVM). A multicenter approach was used to collect data for machine learning training, including behavioral and physiological indicators, age, and reverse Stroop task (RST) data from 108 children with ADHD and 108 typically developing (TD) children. Near-infrared spectroscopy (NIRS) was used to quantify change in prefrontal cortex oxygenated hemoglobin during RST. Verification data were from 62 children with ADHD and 37 TD children from six facilities in Japan. Results: The SVM general performance results showed sensitivity of 88.71%, specificity of 83.78%, and an overall discrimination rate of 86.25%. Conclusion: A SVM using an objective index from RST may be useful as an auxiliary biomarker for diagnosis for children with ADHD.

本文言語English
ページ(範囲)2012-2020
ページ数9
ジャーナルJournal of Attention Disorders
24
14
DOI
出版ステータスPublished - 2020 12 1
外部発表はい

ASJC Scopus subject areas

  • 発達心理学および教育心理学
  • 臨床心理学

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