Explainable early prediction of gestational diabetes biomarkers by combining medical background and wearable devices: a pilot study with a cohort group in South Africa

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dc.contributor.authorNorris, Shane
dc.contributor.authorKolozali, Sefki
dc.contributor.authorWhite, Sara L.
dc.contributor.authorFasli, Maria
dc.contributor.authorvan Heerden, Alastair
dc.date.accessioned2024-08-13T07:08:06Z
dc.date.available2024-08-13T07:08:06Z
dc.date.issued2024
dc.description.abstractThis study aims to explore the potential of Internet of Things (IoT) devices and explainable Artificial Intelligence (AI) techniques in predicting biomarker values associated with GDM when measured 13–16 weeks prior to diagnosis. We developed a system that forecasts biomarkers such as LDL, HDL, triglycerides, cholesterol, HbA1c, and results from the Oral Glucose Tolerance Test (OGTT) including fasting glucose, 1-hour, and 2-hour postload glucose values. These biomarker values are predicted based on sensory measurements collected around week 12 of pregnancy, including continuous glucose levels, short physical movement recordings, and medical background information. To the best of our knowledge, this is the first study to forecast GDM-associated biomarker values 13 to 16 weeks prior to the GDM screening test, using continuous glucose monitoring devices, a wristband for activity detection, and medical background data. We applied machine learning models, specifically Decision Tree and Random Forest Regressors, along with Coupled-Matrix Tensor Factorisation (CMTF) and Elastic Net techniques, examining all possible combinations of these methods across different data modalities. The results demonstrated good performance for most biomarkers. On average, the models achieved Mean Squared Error (MSE) between 0.29 and 0.42 and Mean Absolute Error (MAE) between 0.23 and 0.45 for biomarkers like HDL, LDL, cholesterol, and HbA1c. For the OGTT glucose values, the average MSE ranged from 0.95 to 2.44, and the average MAE ranged from 0.72 to 0.91. Additionally, the utilisation of CMTF with Alternating Least Squares technique yielded slightly better results (0.16 MSE and 0.07 MAE on average) compared to the well-known Elastic Net feature selection technique. While our study was conducted with a limited cohort in South Africa, our findings offer promising indications regarding the potential for predicting biomarker values in pregnant women through the integration of wearable devices and medical background data in the analysis. Nevertheless, further validation on a larger, more diverse cohort is imperative to substantiate these encouraging results.
dc.description.sponsorshipBusiness and Local Government Data Research Centre under Grant ES/L011859/1
dc.description.submitterPM2024
dc.facultyFaculty of Health Sciences
dc.identifier0000-0001-7124-3788
dc.identifier.citationKolozali, Ş., White, S. L., Norris, S., Fasli, M., & Van Heerden, A. (2024). Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study with a Cohort Group in South Africa. IEEE Journal of Biomedical and Health Informatics, 28(4), 1860-1871. https://doi.org/10.1109/JBHI.2024.3361505
dc.identifier.issn2168-2194 (print)
dc.identifier.issn2168-2208 (online)
dc.identifier.other10.1109/JBHI.2024.3361505
dc.identifier.urihttps://hdl.handle.net/10539/40072
dc.journal.titleIEEE Journal of Biomedical and Health Informatics
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.ispartofseriesVol. 28; No. 4
dc.rights© 2024 IEEE.
dc.schoolSchool of Clinical Medicine
dc.subjectInternet of Things healthcare
dc.subjectGestational diabetes mellitus
dc.subjectRemote sensing
dc.subjectCoupled-matrix tensor factorisation
dc.subjectTree-based regressors
dc.subjectExplainable AI models
dc.subject.otherSDG-17: Partnerships for the goals
dc.titleExplainable early prediction of gestational diabetes biomarkers by combining medical background and wearable devices: a pilot study with a cohort group in South Africa
dc.typeArticle
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