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https://doi.org/10.1016/j.diabres.2022.109237
Title: | Population-centric risk prediction modeling for gestational diabetes mellitus: A machine learning approach | Authors: | Kumar, M. Chen, L. Tan, K. Ang, L.T. Ho, C. Wong, G. Soh, S.E. Tan, K.H. Chan, J.K.Y. Godfrey, K.M. Chan, S.-Y. Chong, M.F.F. Connolly, J.E. Chong, Y.S. Eriksson, J.G. Feng, M. Karnani, N. |
Keywords: | Asian populations Gestational Diabetes Mellitus Heterogeneity Machine Learning Non-Invasive UK NICE |
Issue Date: | 3-Feb-2022 | Publisher: | Elsevier Ireland Ltd | Citation: | Kumar, M., Chen, L., Tan, K., Ang, L.T., Ho, C., Wong, G., Soh, S.E., Tan, K.H., Chan, J.K.Y., Godfrey, K.M., Chan, S.-Y., Chong, M.F.F., Connolly, J.E., Chong, Y.S., Eriksson, J.G., Feng, M., Karnani, N. (2022-02-03). Population-centric risk prediction modeling for gestational diabetes mellitus: A machine learning approach. Diabetes Research and Clinical Practice 185 : 109237. ScholarBank@NUS Repository. https://doi.org/10.1016/j.diabres.2022.109237 | Abstract: | Aims: The heterogeneity in Gestational Diabetes Mellitus (GDM) risk factors among different populations impose challenges in developing a generic prediction model. This study evaluates the predictive ability of existing UK NICE guidelines for assessing GDM risk in Singaporean women, and used machine learning to develop a non-invasive predictive model. Methods: Data from 909 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study, Growing Up in Singapore Towards healthy Outcomes (GUSTO), was used for predictive modeling. We used a CatBoost gradient boosting algorithm, and the Shapley feature attribution framework for model building and interpretation of GDM risk attributes. Results: UK NICE guidelines showed poor predictability in Singaporean women [AUC:0.60 (95% CI 0.51, 0.70)]. The non-invasive predictive model comprising of 4 non-invasive factors: mean arterial blood pressure in first trimester, age, ethnicity and previous history of GDM, greatly outperformed [AUC:0.82 (95% CI 0.71, 0.93)] the UK NICE guidelines. Conclusions: The UK NICE guidelines may be insufficient to assess GDM risk in Asian women. Our non-invasive predictive model outperforms the current state-of-the-art machine learning models to predict GDM, is easily accessible and can be an effective approach to minimize the economic burden of universal testing & GDM associated healthcare in Asian populations. © 2022 Elsevier B.V. | Source Title: | Diabetes Research and Clinical Practice | URI: | https://scholarbank.nus.edu.sg/handle/10635/229699 | ISSN: | 0168-8227 | DOI: | 10.1016/j.diabres.2022.109237 |
Appears in Collections: | Staff Publications Elements |
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