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:1.8081

@Article{kumar:2022:IJERPH,

• 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",
• title = "Automated Machine Learning {(AutoML)-Derived} Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus",
• journal = "International Journal of Environmental Research and Public Health",
• year = "2022",
• volume = "19",
• number = "11",
• pages = "Article No. 6792",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1660-4601",
• URL = "https://www.mdpi.com/1660-4601/19/11/6792",
• DOI = "doi:10.3390/ijerph19116792",
• 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.",
• 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