Evaluation of predisposing factors of Diabetes Mellitus post Gestational Diabetes Mellitus using Machine Learning Techniques

Krishnan, D. Radha; Maddipati, Chakravarthy; Menakath, Gayathri P; Anagha, Radhakrishnan,; Himavarshini, Yarrangangu; Aparna, Asok; Mukundan, Kaveri; Pathinarupothi, Rahul Krishnan; Alangot, Bithin; Mahankali, Sirisha

doi:10.1109/scored.2019.8896323

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…Lasso regularization was used for feature selection. Nested standard 5-fold cross validation (CV1) was used for model evaluation [17]. An internal stratified 10-fold cross validation (CV2) was performed on each of the five training folds of CV1 for optimizing the shrinkage parameter in lasso (S1 Fig Logistic regression model was fitted on the training folds in CV1 using the selected features from lasso.…”

Section: Machine Learning Analysismentioning

confidence: 99%

Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes

Periyathambi

Parkhi

Ghebremichael‐Weldeselassie

et al. 2022

PLoS ONE

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Objective The aim of the present study was to identify the factors associated with non-attendance of immediate postpartum glucose test using a machine learning algorithm following gestational diabetes mellitus (GDM) pregnancy. Method A retrospective cohort study of all GDM women (n = 607) for postpartum glucose test due between January 2016 and December 2019 at the George Eliot Hospital NHS Trust, UK. Results Sixty-five percent of women attended postpartum glucose test. Type 2 diabetes was diagnosed in 2.8% and 21.6% had persistent dysglycaemia at 6–13 weeks post-delivery. Those who did not attend postpartum glucose test seem to be younger, multiparous, obese, and continued to smoke during pregnancy. They also had higher fasting glucose at antenatal oral glucose tolerance test. Our machine learning algorithm predicted postpartum glucose non-attendance with an area under the receiver operating characteristic curve of 0.72. The model could achieve a sensitivity of 70% with 66% specificity at a risk score threshold of 0.46. A total of 233 (38.4%) women attended subsequent glucose test at least once within the first two years of delivery and 24% had dysglycaemia. Compared to women who attended postpartum glucose test, those who did not attend had higher conversion rate to type 2 diabetes (2.5% vs 11.4%; p = 0.005). Conclusion Postpartum screening following GDM is still poor. Women who did not attend postpartum screening appear to have higher metabolic risk and higher conversion to type 2 diabetes by two years post-delivery. Machine learning model can predict women who are unlikely to attend postpartum glucose test using simple antenatal factors. Enhanced, personalised education of these women may improve postpartum glucose screening.

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Section: Machine Learning Analysismentioning

confidence: 99%

Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes

Periyathambi

Parkhi

Ghebremichael‐Weldeselassie

et al. 2022

PLoS ONE

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“…• ML model for predicting the risk of suffering diabetes mellitus in life after suffering GDM during pregnancy has been developed in [177] • Authors in [57] created a scalable multi-agent system for healthcare resources allocation and individualized care. Blood glucose control has been used as a case study.…”

Section: Other Pregnancy Processesmentioning

confidence: 99%

Artificial Intelligence in Pregnancy: A Scoping Review

et al. 2020

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Artificial Intelligence has been widely applied to a majority of research areas, including health and medicine. Certain complications or disorders that can appear during pregnancy can endanger the life of both mother and fetus. There is enough scientific literature to support the idea that emotional aspects can be a relevant risk factor in pregnancy (such as anxiety, stress or depression, for instance). This paper presents a scoping review of the scientific literature from the past 12 years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) to identify which methodologies, techniques, algorithms and frameworks are used in Artificial Intelligence and Affective Computing for pregnancy health and well-being. The methodology proposed by Arksey and O'Malley, in conjunction with PRISMA-ScR framework has been used to create this review. Despite the relevance that emotional status can have as a risk factor during pregnancy, one of the main findings of this study is that there is still not a significant amount of literature on automatic analysis of emotion. Health enhancement and well-being for pregnant women can be achieved with artificial intelligence or affective computing based devices, hence future work on this topic is strongly suggested.

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“…In addition, the only available literature to our knowledge that looked at predicting the onset of T2DM following GDM was based on only 77 patient records with 15 variables. 24…”

Section: Discussionmentioning

confidence: 99%

“…The use of machine learning for predicting postpartum prediabetes in GDM-diagnosed women has been rarely studied. We are aware of only one study that has made use of machine learning algorithms to predict the occurrence of T2DM post-GDM: Krishnan et al 24 proposed random forest and gaussian naive Bayes algorithms to predict T2DM after GDM, and achieved a modest specificity of 23% at a sensitivity of 88%. It also lacked the use of advanced techniques to deal with imbalanced data.…”

Section: Discussionmentioning

confidence: 99%

Machine learning prediction of early postpartum prediabetes in women with gestational diabetes mellitus

Parkhi

Periyathambi

Ghebremichael‐Weldeselassie

et al. 2023

Preprint

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Background: Early onset of type 2 diabetes and cardiovascular disease are common complications for women diagnosed with gestational diabetes. About half of the women with gestational diabetes develop postpartum prediabetes within 10 years of the index pregnancy. These women also have double the risk of developing cardiovascular disease than women without a history of gestational diabetes. Currently, there is no accurate way of knowing which women with gestational diabetes are likely to develop postpartum prediabetes. This study aims to predict the risk of postpartum prediabetes in women diagnosed with gestational diabetes. Methods: We build a sparse logistic regression-based machine learning model to learn key variables significant for the prediction of postpartum prediabetes, from antenatal data with maternal anthropometric and biochemical variables as well as neonatal characteristics of 607 UK women diagnosed with gestational diabetes. We evaluate the performance of the proposed model in addition to other more advanced machine learning methods using established metrics such as the area under the receiver operating characteristic curve and specificity for pre-determined values of sensitivity. We use K-L divergence and information graphs to evaluate and compare different thresholds of classification for targeted screening options in resource-constrained settings. We also perform a decision curve analysis to study the net standardized benefit of our model compared to the universal screening approach. Results: Strikingly, our sparse logistic regression approach selects only two variables as relevant but gives an area under the receiver operating characteristic curve of 0.72, outperforming all other methods. It can identify postpartum prediabetes in women with gestational diabetes using the Rule-in test with 92\% specificity at an optimal probability threshold of 0.381 and using the Rule-out test with 92\% sensitivity at an optimal probability threshold of 0.140. Conclusion: We propose a simple logistic regression model, which needs only the antenatal fasting glucose at OGTT and HbA1c soon after the diagnosis of GDM, to predict, with remarkable accuracy, the probability of postpartum prediabetes in women with gestational diabetes. We envision this to be a practical solution, which coupled with a targeted follow-up of high-risk women, could yield better cardiometabolic outcomes in women with a history of GDM.

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Evaluation of predisposing factors of Diabetes Mellitus post Gestational Diabetes Mellitus using Machine Learning Techniques

Cited by 9 publications

References 12 publications

Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes

Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes

Artificial Intelligence in Pregnancy: A Scoping Review

Machine learning prediction of early postpartum prediabetes in women with gestational diabetes mellitus

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