| Autor: |
Zentaro Yamagata, Hiroshi Yokomichi, Tadao Ooka, Hisashi Johno, Kazunori Nakamoto, Yoshioki Yoda |
| Jazyk: |
angličtina |
| Předmět: |
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| Zdroj: |
BMJ Nutrition, Prevention & Health, Vol , Iss |
| Druh dokumentu: |
article |
| ISSN: |
2516-5542 |
| DOI: |
10.1136/bmjnph-2020-000200 |
| Popis: |
Introduction Early intervention in type 2 diabetes can prevent exacerbation of insulin resistance. More effective interventions can be implemented by early and precise prediction of the change in glycated haemoglobin A1c (HbA1c). Artificial intelligence (AI), which has been introduced into various medical fields, may be useful in predicting changes in HbA1c. However, the inability to explain the predictive factors has been a problem in the use of deep learning, the leading AI technology. Therefore, we applied a highly interpretable AI method, random forest (RF), to large-scale health check-up data and examined whether there was an advantage over a conventional prediction model.Research design and methods This study included a cumulative total of 42 908 subjects not receiving treatment for diabetes with an HbA1c |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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