Kumar , M , Ang , L T , Png , H , Ng , M , Tan , K , Loy , S L , Tan , K H , Chan , J K Y , Godfrey , K M , Chan , S , Chong , Y S , Eriksson , J G , Feng , M & Karnani , N 2022 , ' Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus ' , International Journal of Environmental Research and Public Health , vol. 19 , no. 11 , 6792 . https://doi.org/10.3390/ijerph19116792
Title: | Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus |
Author: | Kumar, Mukkesh; Ang, Li Ting; Png, Hang; Ng, Maisie; Tan, Karen; Loy, See Ling; Tan, Kok Hian; Chan, Jerry Kok Yen; Godfrey, Keith M.; Chan, Shiao-yng; Chong, Yap Seng; Eriksson, Johan G.; Feng, Mengling; Karnani, Neerja |
Contributor organization: | Clinicum Research Programs Unit Johan Eriksson / Principal Investigator Department of General Practice and Primary Health Care University of Helsinki |
Date: | 2022-06 |
Language: | eng |
Number of pages: | 17 |
Belongs to series: | International Journal of Environmental Research and Public Health |
ISSN: | 1660-4601 |
DOI: | https://doi.org/10.3390/ijerph19116792 |
URI: | http://hdl.handle.net/10138/346330 |
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. |
Subject: |
Asian populations
digital health gestational diabetes mellitus HbA(1c) machine learning preconception care prediction preterm birth public health risk factors PREGNANCY GLUCOSE HEALTH 3121 General medicine, internal medicine and other clinical medicine 3142 Public health care science, environmental and occupational health |
Peer reviewed: | Yes |
Rights: | cc_by |
Usage restriction: | openAccess |
Self-archived version: | publishedVersion |
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