Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{kumar:2022:IJERPH,
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author = "Mukkesh Kumar and Li Ting Ang and Hang Png and
Maisie Ng and Karen Tan and See Ling Loy and Kok Hian Tan and
Jerry Kok Yen Chan and Keith M. Godfrey and
Shiao-yng Chan and Yap Seng Chong and Johan G. Eriksson and
Mengling Feng and Neerja Karnani",
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title = "Automated Machine Learning {(AutoML)-Derived}
Preconception Predictive Risk Model to Guide Early
Intervention for Gestational Diabetes Mellitus",
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journal = "International Journal of Environmental Research and
Public Health",
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year = "2022",
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volume = "19",
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number = "11",
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pages = "Article No. 6792",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1660-4601",
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URL = "https://www.mdpi.com/1660-4601/19/11/6792",
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DOI = "doi:10.3390/ijerph19116792",
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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 A1c (HbA1c), 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 HbA1c was positively
associated with increased risks of GDM (p = 0.001, odds
ratio (95percent 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
HbA1c 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.",
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notes = "also known as \cite{ijerph19116792}",
- }
Genetic Programming entries for
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
Neerja Karnani
Citations