Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model

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dc.contributor.authorBelsti, Y.
dc.contributor.authorMoran, L.
dc.contributor.authorDu, L.
dc.contributor.authorMousa, A.
dc.contributor.authorDe Silva, K.
dc.contributor.authorEnticott, J.
dc.contributor.authorTeede, H.
dc.date.issued2023
dc.descriptionAvailable online 21 September 2023
dc.description.abstractBackground: Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal. Objective: We aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM. Methods: A supervised machine learning predictive analysis was performed on data from routine antenatal care at a large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed. Results: Upon internal validation, the machine learning and logistic regression model’s area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39). Conclusions: In this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables.
dc.description.statementofresponsibilityYitayeh Belsti, Lisa Moran, Lan Du, Aya Mousa, Kushan De Silva, Joanne Enticott, Helena Teede
dc.identifier.citationInternational Journal of Medical Informatics, 2023; 179:105228-1-105228-12
dc.identifier.doi10.1016/j.ijmedinf.2023.105228
dc.identifier.issn1386-5056
dc.identifier.issn1872-8243
dc.identifier.orcidMoran, L. [0000-0001-5772-6484]
dc.identifier.urihttps://hdl.handle.net/2440/139881
dc.language.isoen
dc.publisherElsevier BV
dc.relation.grantNHMRC
dc.rights© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.source.urihttps://doi.org/10.1016/j.ijmedinf.2023.105228
dc.subjectMachine learning
dc.subjectPredictive model
dc.subjectPrognosis
dc.subjectGestational diabetes mellitus
dc.titleComparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model
dc.typeJournal article
pubs.publication-statusPublished

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