Md Merajul Islam1, Md Jahanur Rahman2, Dulal Chandra Roy3, Md Maniruzzaman4. 1. Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. Electronic address: merajul.stat4811@gmail.com. 2. Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. Electronic address: jahanurmj@gmail.com. 3. Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. Electronic address: dulalroystat@yahoo.com. 4. Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh; Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh. Electronic address: monir.stat91@gmail.com.
Abstract
BACKGROUND AND AIMS: Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based classifiers for automated detection and classification of diabetes. METHODS: The diabetes dataset have taken from Bangladesh demographic and health survey, 2011 data having 1569 respondents are 127 diabetes. Two statistical tests as independent t for continuous and chi-square for categorical variables are used to determine the risk factors of diabetes. Six ML-based classifiers as support vector machine, random forest, linear discriminant analysis, logistic regression, k-nearest neighborhood, bagged classification and regression tree (Bagged CART) have been adopted to predict and classify of diabetes. RESULTS: Our findings show that 11 factors out of 15 factors are significantly associated with diabetes. Bagged CART provides the highest accuracy and area under the curve of 94.3% and 0.600. CONCLUSIONS: Bagged CART anticipates a very supportive computational resource for classification of diabetes and it would be very helpful to the doctors for making a decision to control diabetes disease in Bangladesh.
BACKGROUND AND AIMS: Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based classifiers for automated detection and classification of diabetes. METHODS: The diabetes dataset have taken from Bangladesh demographic and health survey, 2011 data having 1569 respondents are 127 diabetes. Two statistical tests as independent t for continuous and chi-square for categorical variables are used to determine the risk factors of diabetes. Six ML-based classifiers as support vector machine, random forest, linear discriminant analysis, logistic regression, k-nearest neighborhood, bagged classification and regression tree (Bagged CART) have been adopted to predict and classify of diabetes. RESULTS: Our findings show that 11 factors out of 15 factors are significantly associated with diabetes. Bagged CART provides the highest accuracy and area under the curve of 94.3% and 0.600. CONCLUSIONS: Bagged CART anticipates a very supportive computational resource for classification of diabetes and it would be very helpful to the doctors for making a decision to control diabetes disease in Bangladesh.
Authors: Jasjit S Suri; Mahesh A Maindarkar; Sudip Paul; Puneet Ahluwalia; Mrinalini Bhagawati; Luca Saba; Gavino Faa; Sanjay Saxena; Inder M Singh; Paramjit S Chadha; Monika Turk; Amer Johri; Narendra N Khanna; Klaudija Viskovic; Sofia Mavrogeni; John R Laird; Martin Miner; David W Sobel; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Athanase D Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Raghu Kolluri; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Meyypan Sockalingam; Ajit Saxena; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Padukode R Krishnan; Tomaz Omerzu; Subbaram Naidu; Andrew Nicolaides; Kosmas I Paraskevas; Mannudeep Kalra; Zoltán Ruzsa; Mostafa M Fouda Journal: Diagnostics (Basel) Date: 2022-06-24
Authors: Aishwariya Dutta; Md Kamrul Hasan; Mohiuddin Ahmad; Md Abdul Awal; Md Akhtarul Islam; Mehedi Masud; Hossam Meshref Journal: Int J Environ Res Public Health Date: 2022-09-28 Impact factor: 4.614
Authors: S M Jubaidur Rahman; N A M Faisal Ahmed; Md Menhazul Abedin; Benojir Ahammed; Mohammad Ali; Md Jahanur Rahman; Md Maniruzzaman Journal: PLoS One Date: 2021-06-17 Impact factor: 3.240