A novel stacking ensemble for detecting three types of diabetes mellitus using a Saudi Arabian dataset: Pre-diabetes, T1DM, and T2DM

Mohammed Gollapalli; Aisha Alansari; Heba Alkhorasani; Meelaf Alsubaii; Rasha Sakloua; Reem Alzahrani; Mohammed Al-Hariri; Maiadah Alfares; Dania AlKhafaji; Reem Al Argan; Waleed Albaker

doi:10.1016/j.compbiomed.2022.105757

A novel stacking ensemble for detecting three types of diabetes mellitus using a Saudi Arabian dataset: Pre-diabetes, T1DM, and T2DM

Comput Biol Med. 2022 Aug:147:105757. doi: 10.1016/j.compbiomed.2022.105757. Epub 2022 Jun 20.

Authors

Affiliations

¹ Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia. Electronic address: magollapalli@iau.edu.sa.
² Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia. Electronic address: 2180004329@iau.edu.sa.
³ Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia. Electronic address: 2180007152@iau.edu.sa.
⁴ Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia. Electronic address: 2180001038@iau.edu.sa.
⁵ Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia. Electronic address: 2180002151@iau.edu.sa.
⁶ Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia. Electronic address: 2180005319@iau.edu.sa.
⁷ Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia. Electronic address: mtalhariri@iau.edu.sa.
⁸ Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia. Electronic address: mnalfares@iau.edu.sa.
⁹ Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, King Fahad Hospital of the University, Khobar, Saudi Arabia. Electronic address: dmkhafaji@iau.edu.sa.
¹⁰ Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, King Fahad Hospital of the University, Khobar, Saudi Arabia. Electronic address: Rjalarqan@iau.edu.sa.
¹¹ Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, King Fahad Hospital of the University, Khobar, Saudi Arabia. Electronic address: wialbakr@iau.edu.sa.

PMID: 35777087
DOI: 10.1016/j.compbiomed.2022.105757

Abstract

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.

Keywords: Machine learning; Permutation feature importance; Pre-diabetes; Stacking; Type 1 diabetes; Type 2 diabetes.

MeSH terms

Algorithms
Diabetes Mellitus, Type 1*
Diabetes Mellitus, Type 2* / diagnosis
Glucose
Humans
Insulins*
Prediabetic State* / diagnosis
Quality of Life
Saudi Arabia
Support Vector Machine

Substances

Insulins
Glucose