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Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status
Science Journal of Public Health
Volume 1, Issue 1, March 2013, Pages: 39-43
Received: Mar. 29, 2013; Published: Mar. 10, 2013
Views 3588      Downloads 287
Authors
Azizur Rahman, Department of Statistics, Jagannath University, Dhaka-1100, Bangladesh
Karimon Nesha, Department of Disaster management, University of Dhaka, 2School of Business, United International
Mariam Akter, Department of Disaster management, University of Dhaka, 2School of Business, United International
Md. Sheikh Giash Uddin, Department of Statistics, Jagannath University, Dhaka-1100, Bangladesh
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Abstract
Various methods can be applied to build predictive models for the clinical data with binary outcome variables. This research aims to compare and explore the process of constructing common predictive models. Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose level. Demographic, enth-ropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460 participants 133 (28.91%) suffered from DM, 116 (25.27%) had impaired glucose tolerance (IGT) and the remainder 211 (45.86%) were disease free. Among other factors, family history of diabetes and arsenic exposure were found as significant risk factors for developing diabetes mellitus (DM), with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 73.79% of cases with IGT or DM in the training datasets, 70.96% in testing datasets and 70.4% of all subjects. On the other hand, the sensitivities of artificial neural network architecture for training and testing datasets and for all subjects were 83.4%, 82.25% and 84.33% respectively, indicate better performance than binary logistic regression model.
Keywords
Artificial Neural Network (ANN), Binary Logistic (LR), Classification, Diabetes Mellitus (DM)
To cite this article
Azizur Rahman, Karimon Nesha, Mariam Akter, Md. Sheikh Giash Uddin, Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status, Science Journal of Public Health. Vol. 1, No. 1, 2013, pp. 39-43. doi: 10.11648/j.sjph.20130101.16
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