Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations
Middle-Aged and Elderly people today face a variety of health problems as a result of their modern lifestyle, which includes increased work stress, less physical activity, and altered food habits. Because of Complications arising, diabetes has become one of the most frequent, severe, and fatal illne...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024172301 |
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| author | Badriah Alkalifah Muhammad Tariq Shaheen Johrah Alotibi Tahani Alsubait Hosam Alhakami |
| author_facet | Badriah Alkalifah Muhammad Tariq Shaheen Johrah Alotibi Tahani Alsubait Hosam Alhakami |
| author_sort | Badriah Alkalifah |
| collection | DOAJ |
| description | Middle-Aged and Elderly people today face a variety of health problems as a result of their modern lifestyle, which includes increased work stress, less physical activity, and altered food habits. Because of Complications arising, diabetes has become one of the most frequent, severe, and fatal illnesses around the world. Therefore, inaccurate measurements of blood glucose levels can seriously damage vital organs. Several strategies for long-term glucose prediction have been proposed in the literature. Unfortunately, these methods require the patient to identify their daily activities, which can be error-prone, such as meal intake, insulin injection, and emotional aspects. This paper suggests using continuous glucose monitoring (CGM) of 14733 patients, with three assistance factors to predict blood glucose levels independently of other parameters, hence reducing the burden on the patients. To support this an Artificial Neural Network (ANN), Binary Decision Tree (BDT), Linear Regression (LR), Boosting Regression Tree Ensemble (BSTE), Linear Regression with Stochastic Gradient Descent (LRSGD), Stepwise (SW), Support Vector Machine (SVM), and Gaussian process regression (GPR) were investigated. The result indicated that The highest classification accuracy of (92.58%) has been achieved by BDT followed by BSTE (92.04%) and GPR (88.59%). The obtained average of root means square error (MSE) was 1.64, 1.67, 1.69, mg/dL for prediction horizon (PH) respectively to GPR, BSTE, and ANN. |
| format | Article |
| id | doaj-art-3c92064b71904eb79779946e0b328bc8 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-3c92064b71904eb79779946e0b328bc82025-01-17T04:50:29ZengElsevierHeliyon2405-84402025-01-01111e41199Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuationsBadriah Alkalifah0Muhammad Tariq Shaheen1Johrah Alotibi2Tahani Alsubait3Hosam Alhakami4Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi ArabiaSchool of Electrical Engineering and Computer Science, NUST, Islamabad, PakistanDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia; Corresponding author.Department of Software Engineering, College of Computing, Umm Al-Qura University, Makkah, Saudi ArabiaMiddle-Aged and Elderly people today face a variety of health problems as a result of their modern lifestyle, which includes increased work stress, less physical activity, and altered food habits. Because of Complications arising, diabetes has become one of the most frequent, severe, and fatal illnesses around the world. Therefore, inaccurate measurements of blood glucose levels can seriously damage vital organs. Several strategies for long-term glucose prediction have been proposed in the literature. Unfortunately, these methods require the patient to identify their daily activities, which can be error-prone, such as meal intake, insulin injection, and emotional aspects. This paper suggests using continuous glucose monitoring (CGM) of 14733 patients, with three assistance factors to predict blood glucose levels independently of other parameters, hence reducing the burden on the patients. To support this an Artificial Neural Network (ANN), Binary Decision Tree (BDT), Linear Regression (LR), Boosting Regression Tree Ensemble (BSTE), Linear Regression with Stochastic Gradient Descent (LRSGD), Stepwise (SW), Support Vector Machine (SVM), and Gaussian process regression (GPR) were investigated. The result indicated that The highest classification accuracy of (92.58%) has been achieved by BDT followed by BSTE (92.04%) and GPR (88.59%). The obtained average of root means square error (MSE) was 1.64, 1.67, 1.69, mg/dL for prediction horizon (PH) respectively to GPR, BSTE, and ANN.http://www.sciencedirect.com/science/article/pii/S2405844024172301DiabeticContinuous glucose monitoring (CGM)HypoglycemiaHyperglycemiaRegressionMachine learning (ML) |
| spellingShingle | Badriah Alkalifah Muhammad Tariq Shaheen Johrah Alotibi Tahani Alsubait Hosam Alhakami Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations Heliyon Diabetic Continuous glucose monitoring (CGM) Hypoglycemia Hyperglycemia Regression Machine learning (ML) |
| title | Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations |
| title_full | Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations |
| title_fullStr | Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations |
| title_full_unstemmed | Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations |
| title_short | Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations |
| title_sort | evaluation of machine learning based regression techniques for prediction of diabetes levels fluctuations |
| topic | Diabetic Continuous glucose monitoring (CGM) Hypoglycemia Hyperglycemia Regression Machine learning (ML) |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024172301 |
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