Kolozali, Sefki and Fasli, Maria and White, Sara L and Norris, Shane and van Heerden, Alastair (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). pp. 1860-1871. DOI https://doi.org/10.1109/jbhi.2024.3361505 (In Press)
Kolozali, Sefki and Fasli, Maria and White, Sara L and Norris, Shane and van Heerden, Alastair (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). pp. 1860-1871. DOI https://doi.org/10.1109/jbhi.2024.3361505 (In Press)
Kolozali, Sefki and Fasli, Maria and White, Sara L and Norris, Shane and van Heerden, Alastair (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). pp. 1860-1871. DOI https://doi.org/10.1109/jbhi.2024.3361505 (In Press)
Abstract
This 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 post- load 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 se- lection 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.
Item Type: | Article |
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Uncontrolled Keywords: | Pregnancy; Glucose; Biomarkers; Diabetes; Medical diagnostic imaging; Bioinformatics; Tensors; Internet of Things healthcare; gestational diabetes mellitus; remote sensing; coupled-matrix tensor factorisation; tree-based regressors; explainable AI models |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 05 Feb 2024 15:13 |
Last Modified: | 07 Aug 2024 16:01 |
URI: | http://repository.essex.ac.uk/id/eprint/37698 |
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