Development of an alternative growth curve which is concerned with the prevention of lifestyle diseases through deep learning
| Project/Area Number |
18K11021
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 59040:Nutrition science and health science-related
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| Research Institution | Kagawa University (2020-2021)
Okayama University (2018-2019) |
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Principal Investigator |
Haga Chiyori 香川大学, 医学部, 教授 (30432157)
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| Co-Investigator(Kenkyū-buntansha) |
相田 敏明 岡山大学, ヘルスシステム統合科学研究科, 講師 (60290722)
珠玖 隆行 岡山大学, 環境生命科学研究科, 准教授 (70625053)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | ディープラーニング / ライフコースアプローチ / 生活習慣病予防 / 体格推移 / アディポシティリバウンド / 小児保健 / 体格予測 / 小児期 / 成長曲線 / 生活主観病予防 / 機械学習 / k-meas法 / Body Mass Index |
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| Outline of Final Research Achievements |
Lifestyle-related diseases are a global health issue, but effective remedies have not been established. We worked on this research subject in order to build evidence of the approach strategy by the life course approach. The task is divided into three goals: Goal 1: Construction of a system for big data collection, Goal 2: Improvement of accuracy of pattern classification of physique transition in childhood and physique prediction after adolescence by adiposity rebound (AR) Examination of ability, goal 3. It was a study of domestic and foreign guidelines for an approach starting from the embryonic period. As a result, the physique of Japanese children can be classified into 4 boys and 3 girls, and the AR period is 3 to 7 years old, and AR under 6 years old may significantly increase the risk of developing obesity in early adolescence. Indicated.
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| Academic Significance and Societal Importance of the Research Achievements |
ディープラーニング（機械学習）を用いて小児期の子どもの体格推移を分類し，これまでの分類の精度を向上させたこと。その結果，男女ともにパターンは３から4程度であることがおおよそ妥当であると判断できた。また，日本人小児のアディポシティリバウンドの時期を特定し，早期・後期を6歳前後で見た結果，14歳時点の肥満発症と有意にかかわることが明らかになり，幼児期からの生活習慣病予防の必要性を示唆することができた。
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Report
(5 results)
Research Products
(21 results)
