| Autor: |
Hwang MS; College of Nursing, Woosuk University, Wanju, Republic of Korea., Song E; AI Convergence Education, College of Education, Dongguk University, Seoul, Republic of Korea., Ahn J; Department of Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea., Park S; Department of Nursing Science, Chungbuk National University, Chungbuk, Republic of Korea. |
| Jazyk: |
angličtina |
| Zdroj: |
Metabolic syndrome and related disorders [Metab Syndr Relat Disord] 2023 Nov; Vol. 21 (9), pp. 479-488. Date of Electronic Publication: 2023 Sep 05. |
| DOI: |
10.1089/met.2023.0043 |
| Abstrakt: |
Objective: Current evidence regarding the safety of abdominal subcutaneous injections in pregnant women is limited. In this study, we developed a predictive model for abdominal skin-subcutaneous fat thickness (S-ScFT) by gestational periods (GP) in pregnant women. Methods: A total of 354 cases were measured for S-ScFT. Three machine learning algorithms, namely deep learning, random forest, and support vector machine, were used for S-ScFT predictive modeling and factor analysis for each abdominal site. Data analysis was performed using SPSS and RapidMiner softwares. Results: The deep learning algorithm best predicted the abdominal S-ScFT. The common important variables in all three algorithms for the prediction of abdominal S-ScFT were menarcheal age, prepregnancy weight, prepregnancy body mass index (categorized), large fetus for gestational age, and alcohol consumption. Conclusion: Predicting the safety of subcutaneous injections during pregnancy could be beneficial for managing gestational diabetes mellitus in pregnant women. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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