Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus

Mukkesh Kumar, Li Ting Ang, Hang Png, Maisie Ng, Karen Tan, See Ling Loy, Kok Hian Tan, Jerry Kok Yen Chan, Keith M. Godfrey, Shiao-yng Chan, Yap Seng Chong, Johan G. Eriksson, Mengling Feng and Neerja Karnani
Additional contact information
Mukkesh Kumar: Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore
Li Ting Ang: Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore
Hang Png: Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore
Maisie Ng: Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore
Karen Tan: Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore
See Ling Loy: Obstetrics & Gynaecology Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
Kok Hian Tan: Obstetrics & Gynaecology Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
Jerry Kok Yen Chan: Obstetrics & Gynaecology Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
Keith M. Godfrey: MRC Lifecourse Epidemiology Centre, NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, University of Southampton, Southampton SO17 1BJ, UK
Shiao-yng Chan: Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore
Yap Seng Chong: Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore
Johan G. Eriksson: Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore
Mengling Feng: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 119077, Singapore
Neerja Karnani: Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore

IJERPH, 2022, vol. 19, issue 11, 1-17

Abstract: The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with limited success due to late implementation, poor adherence and generic guidelines. In this study, we aimed to build a preconception-based GDM predictor to enable early intervention. We also assessed the associations of top predictors with GDM and adverse birth outcomes. Our evolutionary algorithm-based automated machine learning (AutoML) model was implemented with data from 222 Asian multi-ethnic women in a preconception cohort study, Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO). A stacked ensemble model with a gradient boosting classifier and linear support vector machine classifier (stochastic gradient descent training) was derived using genetic programming, achieving an excellent AUC of 0.93 based on four features (glycated hemoglobin A 1c (HbA 1c ), mean arterial blood pressure, fasting insulin, triglycerides/HDL ratio). The results of multivariate logistic regression model showed that each 1 mmol/mol increase in preconception HbA 1c was positively associated with increased risks of GDM ( p = 0.001, odds ratio (95% CI) 1.34 (1.13–1.60)) and preterm birth ( p = 0.011, odds ratio 1.63 (1.12–2.38)). Optimal control of preconception HbA 1c may aid in preventing GDM and reducing the incidence of preterm birth. Our trained predictor has been deployed as a web application that can be easily employed in GDM intervention programs, prior to conception.

Keywords: Asian populations; digital health; gestational diabetes mellitus; HbA 1c; machine learning; preconception care; prediction; preterm birth; public health; risk factors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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