A novel few shot learning derived architecture for long-term HbA1c prediction
<p dir="ltr">Regular monitoring of glycated hemoglobin (HbA1c) levels is important for the proper management of diabetes. Studies demonstrated that lower levels of HbA1c play an essential role in reducing or delaying microvascular difficulties that arise from diabetes. In addition, t...
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2024
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| author | Marwa Qaraqe (10135172) |
| author2 | Almiqdad Elzein (13141038) Samir Belhaouari (18418839) Md Shafiq Ilam (19206097) Goran Petrovski (129836) |
| author2_role | author author author author |
| author_facet | Marwa Qaraqe (10135172) Almiqdad Elzein (13141038) Samir Belhaouari (18418839) Md Shafiq Ilam (19206097) Goran Petrovski (129836) |
| author_role | author |
| dc.creator.none.fl_str_mv | Marwa Qaraqe (10135172) Almiqdad Elzein (13141038) Samir Belhaouari (18418839) Md Shafiq Ilam (19206097) Goran Petrovski (129836) |
| dc.date.none.fl_str_mv | 2024-01-04T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-023-50348-1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_novel_few_shot_learning_derived_architecture_for_long-term_HbA1c_prediction/26363242 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning Biomarkers Biotechnology Endocrinology Engineering Health care Mathematics and computing |
| dc.title.none.fl_str_mv | A novel few shot learning derived architecture for long-term HbA1c prediction |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Regular monitoring of glycated hemoglobin (HbA1c) levels is important for the proper management of diabetes. Studies demonstrated that lower levels of HbA1c play an essential role in reducing or delaying microvascular difficulties that arise from diabetes. In addition, there is an association between elevated HbA1c levels and the development of diabetes-related comorbidities. The advanced prediction of HbA1c enables patients and physicians to make changes to treatment plans and lifestyle to avoid elevated HbA1c levels, which can consequently lead to irreversible health complications. Despite the impact of such prediction capabilities, no work in the literature or industry has investigated the futuristic prediction of HbA1c using current blood glucose (BG) measurements. For the first time in the literature, this work proposes a novel FSL-derived algorithm for the long-term prediction of clinical HbA1c measures. More importantly, the study specifically targeted the pediatric Type-1 diabetic population, as an early prediction of elevated HbA1c levels could help avert severe life-threatening complications in these young children. Short-term CGM time-series data are processed using both novel image transformation approaches, as well as using conventional signal processing methods. The derived images are then fed into a convolutional neural network (CNN) adapted from a few-shot learning (FSL) model for feature extraction, and all the derived features are fused together. A novel normalized FSL-distance (FSLD) metric is proposed for accurately separating the features of different HbA1c levels. Finally, a K-nearest neighbor (KNN) model with majority voting is implemented for the final classification task. The proposed FSL-derived algorithm provides a prediction accuracy of 93.2%.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-023-50348-1" target="_blank">https://dx.doi.org/10.1038/s41598-023-50348-1</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_03343b84ff4d19f5a4b8d50ce824c9ed |
| identifier_str_mv | 10.1038/s41598-023-50348-1 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26363242 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A novel few shot learning derived architecture for long-term HbA1c predictionMarwa Qaraqe (10135172)Almiqdad Elzein (13141038)Samir Belhaouari (18418839)Md Shafiq Ilam (19206097)Goran Petrovski (129836)Biomedical and clinical sciencesClinical sciencesHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningBiomarkersBiotechnologyEndocrinologyEngineeringHealth careMathematics and computing<p dir="ltr">Regular monitoring of glycated hemoglobin (HbA1c) levels is important for the proper management of diabetes. Studies demonstrated that lower levels of HbA1c play an essential role in reducing or delaying microvascular difficulties that arise from diabetes. In addition, there is an association between elevated HbA1c levels and the development of diabetes-related comorbidities. The advanced prediction of HbA1c enables patients and physicians to make changes to treatment plans and lifestyle to avoid elevated HbA1c levels, which can consequently lead to irreversible health complications. Despite the impact of such prediction capabilities, no work in the literature or industry has investigated the futuristic prediction of HbA1c using current blood glucose (BG) measurements. For the first time in the literature, this work proposes a novel FSL-derived algorithm for the long-term prediction of clinical HbA1c measures. More importantly, the study specifically targeted the pediatric Type-1 diabetic population, as an early prediction of elevated HbA1c levels could help avert severe life-threatening complications in these young children. Short-term CGM time-series data are processed using both novel image transformation approaches, as well as using conventional signal processing methods. The derived images are then fed into a convolutional neural network (CNN) adapted from a few-shot learning (FSL) model for feature extraction, and all the derived features are fused together. A novel normalized FSL-distance (FSLD) metric is proposed for accurately separating the features of different HbA1c levels. Finally, a K-nearest neighbor (KNN) model with majority voting is implemented for the final classification task. The proposed FSL-derived algorithm provides a prediction accuracy of 93.2%.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-023-50348-1" target="_blank">https://dx.doi.org/10.1038/s41598-023-50348-1</a></p>2024-01-04T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-023-50348-1https://figshare.com/articles/journal_contribution/A_novel_few_shot_learning_derived_architecture_for_long-term_HbA1c_prediction/26363242CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/263632422024-01-04T03:00:00Z |
| spellingShingle | A novel few shot learning derived architecture for long-term HbA1c prediction Marwa Qaraqe (10135172) Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning Biomarkers Biotechnology Endocrinology Engineering Health care Mathematics and computing |
| status_str | publishedVersion |
| title | A novel few shot learning derived architecture for long-term HbA1c prediction |
| title_full | A novel few shot learning derived architecture for long-term HbA1c prediction |
| title_fullStr | A novel few shot learning derived architecture for long-term HbA1c prediction |
| title_full_unstemmed | A novel few shot learning derived architecture for long-term HbA1c prediction |
| title_short | A novel few shot learning derived architecture for long-term HbA1c prediction |
| title_sort | A novel few shot learning derived architecture for long-term HbA1c prediction |
| topic | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning Biomarkers Biotechnology Endocrinology Engineering Health care Mathematics and computing |