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Your search keyword '"Rahman, Md. Jahanur"' showing total 23 results
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23 results on '"Rahman, Md. Jahanur"'

12. Identification of the risk factors of type 2 diabetes and its prediction using machine learning techniques

Author

Islam, Md. Merajul, Rahman, Md. Jahanur, Menhazul Abedin, Md., Ahammed, Benojir, Ali, Mohammad, Ahmed, N.A.M Faisal, and Maniruzzaman, Md.

Abstract

ABSTRACTThis study identified the risk factors for type 2 diabetes (T2D) and proposed a machine learning (ML) technique for predicting T2D. The risk factors for T2D were identified by multiple logistic regression (MLR) using p-value (p<0.05). Then, five ML-based techniques, including logistic regression, naïve Bayes, J48, multilayer perceptron, and random forest (RF) were employed to predict T2D. This study utilized two publicly available datasets, derived from the National Health and Nutrition Examination Survey, 2009-2010 and 2011-2012. About 4922 respondents with 387 T2D patients were included in 2009-2010 dataset, whereas 4936 respondents with 373 T2D patients were included in 2011-2012. This study identified six risk factors (age, education, marital status, SBP, smoking, and BMI) for 2009-2010 and nine risk factors (age, race, marital status, SBP, DBP, direct cholesterol, physical activity, smoking, and BMI) for 2011-2012. RF-based classifier obtained 95.9% accuracy, 95.7% sensitivity, 95.3% F-measure, and 0.946 area under the curve.

Published
2023
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22. An Empirical Study on Long-Run Neutrality of Money in the Japanese Economy.

Author

Rahman, Md. Jahanur and Toyoda, Toshihisa

Subjects
MONEY, HYPOTHESIS, ECONOMIC seasonal variations, GROSS domestic product, MONEY supply, SUPPLY & demand, TIME series analysis, STATISTICS
Abstract

Previous tests of the long-run neutrality of money hypothesis have generally relied on seasonally adjusted data and overlooked the important issues of seasonality. This article analyzes the long-run neutrality of money in Japan using quarterly seasonally unadjusted data, which permit an examination of the effects of seasonality and the robustness of previous empirical results. Fisher and Seater (1993) methodology is used with both seasonally unadjusted and adjusted Japanese real gross domestic product and nominal money supply to test the long-run neutrality of money hypothesis. Using two measures of money stock, namely, M1 and M2, it is shown that the hypothesis is supported using M2 as the measure of money supply, while it is rejected using M1. [ABSTRACT FROM AUTHOR]

Published
2008
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23. Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.

Author

Islam MM, Rahman MJ, Chandra Roy D, and Maniruzzaman M

Subjects
Adult, Area Under Curve, Bangladesh epidemiology, Demography, Diabetes Mellitus epidemiology, Diabetes Mellitus, Type 2 classification, Diabetes Mellitus, Type 2 diagnosis, Diabetes Mellitus, Type 2 epidemiology, Discriminant Analysis, Factor Analysis, Statistical, Female, Humans, Logistic Models, Male, Middle Aged, Reproducibility of Results, Support Vector Machine, Treatment Outcome, Automation, Diabetes Mellitus classification, Diabetes Mellitus diagnosis, Health Surveys methods, Machine Learning
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., Competing Interests: Declaration of competing interest The authors have no conflict of interest to declare., (Copyright © 2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.)

Published
2020
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