| Autor: |
Balani, J., Hyer, S. L., Shehata, H., Mohareb, F. |
| Předmět: |
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| Zdroj: |
Obstetric Medicine (1753-495X); Sep2018, Vol. 11 Issue 3, p121-125, 5p, 2 Diagrams, 1 Graph |
| Abstrakt: |
Objective To develop a model to predict gestational diabetes mellitus incorporating classical and a novel risk factor, visceral fat mass. Methods Three hundred two obese non-diabetic pregnant women underwent body composition analysis at booking by bioimpedance analysis. Of this cohort, 72 (24%) developed gestational diabetes mellitus. Principal component analysis was initially performed to identify possible clustering of the gestational diabetes mellitus and non-GDM groups. A machine learning algorithm was then applied to develop a GDM predictive model utilising random forest and decision tree modelling. Results The predictive model was trained on 227 samples and validated using an independent testing subset of 75 samples where the model achieved a validation prediction accuracy of 77.53%. According to the decision tree developed, visceral fat mass emerged as the most important variable in determining the risk of gestational diabetes mellitus. Conclusions We present a model incorporating visceral fat mass, which is a novel risk factor in predicting gestational diabetes mellitus in obese pregnant women. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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