Machine Learning-Based Prediction of Abdominal Subcutaneous Fat Thickness During Pregnancy-Reference-Cited by-同舟云学术

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Machine Learning-Based Prediction of Abdominal Subcutaneous Fat Thickness During Pregnancy

Published:2023-11-01 Issue:9 Volume:21 Page:479-488
ISSN:1540-4196
Container-title:Metabolic Syndrome and Related Disorders
language:en
Short-container-title:Metabolic Syndrome and Related Disorders

Author:

Hwang Moon Sook¹,Song Eunjeong²,Ahn Jeonghee³^ORCID,Park Seungmi⁴^ORCID

Affiliation:

1. College of Nursing, Woosuk University, Wanju, Republic of Korea.

2. AI Convergence Education, College of Education, Dongguk University, Seoul, Republic of Korea.

3. Department of Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea.

4. Department of Nursing Science, Chungbuk National University, Chungbuk, Republic of Korea.

Publisher

Mary Ann Liebert Inc

Subject

Endocrinology, Diabetes and Metabolism,Internal Medicine

Link

https://www.liebertpub.com/doi/pdf/10.1089/met.2023.0043

Reference24 articles.

1. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition

2. Diagnosis and Medical Management of Gestational Diabetes

3. 3. Korean Society of Obstetrics and Gynecology. Obstetrics. 6th ed. Koonja Publishing, Inc.: Paju; 2019.

4. Gestational diabetes risk factors and long-term consequences for both mother and offspring: a literature review

5. Needs for Development of IT-based Nutritional Management Program for Women with Gestational Diabetes Mellitus

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