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

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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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