Cited 0 times in
Comparisons of the prediction models for undiagnosed diabetes between machine learning versus traditional statistical methods
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이용호 | - |
dc.contributor.author | 지선하 | - |
dc.date.accessioned | 2023-08-23T00:19:17Z | - |
dc.date.available | 2023-08-23T00:19:17Z | - |
dc.date.issued | 2023-08 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/196215 | - |
dc.description.abstract | We compared the prediction performance of machine learning-based undiagnosed diabetes prediction models with that of traditional statistics-based prediction models. We used the 2014-2020 Korean National Health and Nutrition Examination Survey (KNHANES) (N = 32,827). The KNHANES 2014-2018 data were used as training and internal validation sets and the 2019-2020 data as external validation sets. The receiver operating characteristic curve area under the curve (AUC) was used to compare the prediction performance of the machine learning-based and the traditional statistics-based prediction models. Using sex, age, resting heart rate, and waist circumference as features, the machine learning-based model showed a higher AUC (0.788 vs. 0.740) than that of the traditional statistical-based prediction model. Using sex, age, waist circumference, family history of diabetes, hypertension, alcohol consumption, and smoking status as features, the machine learning-based prediction model showed a higher AUC (0.802 vs. 0.759) than the traditional statistical-based prediction model. The machine learning-based prediction model using features for maximum prediction performance showed a higher AUC (0.819 vs. 0.765) than the traditional statistical-based prediction model. Machine learning-based prediction models using anthropometric and lifestyle measurements may outperform the traditional statistics-based prediction models in predicting undiagnosed diabetes. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Diabetes Mellitus* / diagnosis | - |
dc.subject.MESH | Diabetes Mellitus* / epidemiology | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Machine Learning | - |
dc.subject.MESH | Models, Statistical | - |
dc.subject.MESH | Nutrition Surveys | - |
dc.subject.MESH | ROC Curve | - |
dc.title | Comparisons of the prediction models for undiagnosed diabetes between machine learning versus traditional statistical methods | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Seong Gyu Choi | - |
dc.contributor.googleauthor | Minsuk Oh | - |
dc.contributor.googleauthor | Dong-Hyuk Park | - |
dc.contributor.googleauthor | Byeongchan Lee | - |
dc.contributor.googleauthor | Yong-Ho Lee | - |
dc.contributor.googleauthor | Sun Ha Jee | - |
dc.contributor.googleauthor | Justin Y Jeon | - |
dc.identifier.doi | 10.1038/s41598-023-40170-0 | - |
dc.contributor.localId | A02989 | - |
dc.contributor.localId | A03965 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 37567907 | - |
dc.contributor.alternativeName | Lee, Yong Ho | - |
dc.contributor.affiliatedAuthor | 이용호 | - |
dc.contributor.affiliatedAuthor | 지선하 | - |
dc.citation.volume | 13 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 13101 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.13(1) : 13101, 2023-08 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.