A novel stacking ensemble for detecting three types of diabetes mellitus using a Saudi Arabian dataset : Pre-diabetes, T1DM, and T2DM
Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved..
Glucose is the primary source of energy for cells, which are the building blocks of life. It is given to the body by insulin that carries out the metabolic tasks that keep people alive. Glucose level imbalance is a sign of diabetes mellitus (DM), a common type of chronic disease. It leads to long-term complications, such as blindness, kidney failure, and heart disease, having a negative impact on one's quality of life. In Saudi Arabia, a ten-fold increase in diabetic cases has been documented within the last three years. DM is broadly categorized as Type 1 Diabetes (T1DM), Type 2 Diabetes (T2DM), and Pre-diabetes. The diagnosis of the correct type is sometimes ambiguous to medical professionals causing difficulties in managing the illness progression. Intensive efforts have been made to predict T2DM. However, there is a lack of studies focusing on accurately identifying T1DM and Pre-diabetes. Therefore, this study aims to utilize Machine Learning (ML) to distinguish and predict the three types of diabetes based on a Saudi Arabian hospital dataset to control their progression. Four different experiments have been conducted to achieve the highest results, where several algorithms were used, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (K-NN), Decision Tree (DT), Bagging, and Stacking. In experiments 2, 3, and 4, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. The empirical results demonstrated promising results of the novel Stacking model that combined Bagging K-NN, Bagging DT, and K-NN, with a K-NN meta-classifier attaining an accuracy, weighted recall, weighted precision, and cohen's kappa score of 94.48%, 94.48%, 94.70%, and 0.9172, respectively. Five principal features were identified to significantly affect the model accuracy using the permutation feature importance, namely Education, AntiDiab, Insulin, Nutrition, and Sex.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:147 |
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| Enthalten in: |
Computers in biology and medicine - 147(2022) vom: 19. Aug., Seite 105757 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gollapalli, Mohammed [VerfasserIn] |
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| Links: |
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| Themen: |
Glucose |
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| Anmerkungen: |
Date Completed 13.07.2022 Date Revised 16.09.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2022.105757 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 100 | 1 | |a Gollapalli, Mohammed |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A novel stacking ensemble for detecting three types of diabetes mellitus using a Saudi Arabian dataset |b Pre-diabetes, T1DM, and T2DM |
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| 520 | |a Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved. | ||
| 520 | |a Glucose is the primary source of energy for cells, which are the building blocks of life. It is given to the body by insulin that carries out the metabolic tasks that keep people alive. Glucose level imbalance is a sign of diabetes mellitus (DM), a common type of chronic disease. It leads to long-term complications, such as blindness, kidney failure, and heart disease, having a negative impact on one's quality of life. In Saudi Arabia, a ten-fold increase in diabetic cases has been documented within the last three years. DM is broadly categorized as Type 1 Diabetes (T1DM), Type 2 Diabetes (T2DM), and Pre-diabetes. The diagnosis of the correct type is sometimes ambiguous to medical professionals causing difficulties in managing the illness progression. Intensive efforts have been made to predict T2DM. However, there is a lack of studies focusing on accurately identifying T1DM and Pre-diabetes. Therefore, this study aims to utilize Machine Learning (ML) to distinguish and predict the three types of diabetes based on a Saudi Arabian hospital dataset to control their progression. Four different experiments have been conducted to achieve the highest results, where several algorithms were used, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (K-NN), Decision Tree (DT), Bagging, and Stacking. In experiments 2, 3, and 4, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. The empirical results demonstrated promising results of the novel Stacking model that combined Bagging K-NN, Bagging DT, and K-NN, with a K-NN meta-classifier attaining an accuracy, weighted recall, weighted precision, and cohen's kappa score of 94.48%, 94.48%, 94.70%, and 0.9172, respectively. Five principal features were identified to significantly affect the model accuracy using the permutation feature importance, namely Education, AntiDiab, Insulin, Nutrition, and Sex | ||
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| 650 | 4 | |a Permutation feature importance | |
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| 650 | 4 | |a Type 1 diabetes | |
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| 700 | 1 | |a Alkhorasani, Heba |e verfasserin |4 aut | |
| 700 | 1 | |a Alsubaii, Meelaf |e verfasserin |4 aut | |
| 700 | 1 | |a Sakloua, Rasha |e verfasserin |4 aut | |
| 700 | 1 | |a Alzahrani, Reem |e verfasserin |4 aut | |
| 700 | 1 | |a Al-Hariri, Mohammed |e verfasserin |4 aut | |
| 700 | 1 | |a Alfares, Maiadah |e verfasserin |4 aut | |
| 700 | 1 | |a AlKhafaji, Dania |e verfasserin |4 aut | |
| 700 | 1 | |a Al Argan, Reem |e verfasserin |4 aut | |
| 700 | 1 | |a Albaker, Waleed |e verfasserin |4 aut | |
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