| ○種別 (必須): | □ | 学術論文 (審査論文)
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| ○言語 (必須): | □ | 英語
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| ○招待 (推奨): |
| ○審査 (推奨): | □ | Peer Review
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| ○カテゴリ (推奨): | □ | 研究
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| ○共著種別 (推奨): | □ | 国内共著 (徳島大学内研究者と国内(学外)研究者との共同研究 (国外研究者を含まない))
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| ○学究種別 (推奨): |
| ○組織 (推奨): |
| ○著者 (必須): | 1. | (英) Hiroe Seto (日) (読)
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| 2. | (英) Asuka Oyama (日) (読)
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| 3. | (英) Shuji Kitora (日) (読)
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| 4. | (英) Hiroshi Toki (日) (読)
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| 5. | (英) Ryohei Yamamoto (日) (読)
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| 6. | 芳賀 昭弘 ([徳島大学.大学院医歯薬学研究部.保健学域.保健科学部門.放射線科学系.医用画像物理学])
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| 7. | (英) Maki Shinzawa (日) (読)
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| 8. | (英) Miyae Yamakawa (日) (読)
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| 9. | (英) Sakiko Fukui (日) (読)
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| 10. | (英) Toshiki Moriyama (日) (読)
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| ○題名 (必須): | □ | (英) Gradient Boosting Decision Tree Becomes More Reliable Than Logistic Regression in Predicting Probability for Diabetes With Big Data (日)
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| ○副題 (任意): |
| ○要約 (任意): | □ | (英) We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than [Formula: see text]. Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes. (日)
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| ○キーワード (推奨): | 1. | (英) Big Data (日) (読)
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| 2. | (英) Decision Trees (日) (読)
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| 3. | (英) Diabetes Mellitus (日) (読)
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| 4. | (英) Female (日) (読)
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| 5. | (英) Humans (日) (読)
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| 6. | (英) Logistic Models (日) (読)
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| 7. | (英) Male (日) (読)
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| 8. | (英) Reproducibility of Results (日) (読)
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| ○発行所 (推奨): |
| ○誌名 (必須): | □ | Scientific Reports ([Nature Publishing Group])
(eISSN: 2045-2322)
| ○ISSN (任意): | □ | 2045-2322
ISSN: 2045-2322
(eISSN: 2045-2322) Title: Scientific reportsTitle(ISO): Sci RepPublisher: Nature Portfolio (NLM Catalog)
(Scopus)
(CrossRef)
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| ○巻 (必須): | □ |
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| ○頁 (必須): | □ |
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| ○都市 (任意): |
| ○年月日 (必須): | □ | 西暦 2022年 9月 初日 (令和 4年 9月 初日)
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| ○URL (任意): |
| ○DOI (任意): | □ | 10.1038/s41598-022-20149-z (→Scopusで検索)
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| ○PMID (任意): | □ | 36220875 (→Scopusで検索)
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| ○CRID (任意): |
| ○Scopus (任意): | 1. | 2-s2.0-85139687144
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| 2. | 2-s2.0-85145399159
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| ○備考 (任意): | 1. | (英) Article.ELocationID: 10.1038/s41598-022-20149-z (日)
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| 2. | (英) Article.PublicationTypeList.PublicationType: Journal Article (日)
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