Application of Connectionist Approach in Classification of Nutritional Status Among Arsenic Affected people in Rural Areas in Bangladesh

A Rahman, M Akter, AK Majumder

Abstract

Various methods can be applied to build predictive models for the clinical data with binary outcome variables. This research aims to explore and compare the process of constructing common predictive models. Models based on an artificial neural network (the connectionist approach) and binary logistic regressions were compared in their ability to classifying malnourished subjects and those with over-weighted participants in rural areas of Bangladesh. Subjects were classified according to the indicator of nutritional status measured by body mass index (BMI). This study also investigated the effects of different factors on the BMI level of a sample population of 460 adults of six villages in Bangladesh. Demographic, enthropometric 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(140 male and 320 females) participants 186(40.44%) were identified as malnourished (BMI<18.5 gm), and the remainder 274(59.56%) were found as over-weighted (BMI>18.5 gm). Among other factors, arsenic exposures were found as significant risk factors for low body mass index (BMI) with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 72.85% of cases with malnourished in the training datasets, 76.08% in the testing datasets and 75.26% of all subjects. The sensitivities of the neural network architecture for the training and testing datasets and for all subjects were 84.28%, 84.78% and 81.72% respectively, indicate better performance than binary logistic regression model.

DOI: http://dx.doi.org/10.3329/akmmcj.v5i2.21128

Anwer Khan Modern Medical College Journal Vol. 5, No. 2: July 2014, Pages 23-29

Keywords

Artificial Neural Network (ANN); Binary Logistic regression; classification; malnourished; over-weighted

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DOI: http://dx.doi.org/10.3329/akmmcj.v5i2.21128

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