Assessing the efficiency of multilayer feed-forward neural network model: application to body mass index data
A variety of statistical approaches have been used to find the directed dependencies among a set of interest variables and to identify the associated important factors. Among the most popular methods are proportional hazard regression and logistic regression. The aim of the current study is to sugge...
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my.utm.447492017-09-05T04:20:15Z http://eprints.utm.my/id/eprint/44749/ Assessing the efficiency of multilayer feed-forward neural network model: application to body mass index data Mohamed, Norizan W. Ahmad, W. M. A. Aleng, Nor Azlida Ahmad, Maizah Hura QA76 Computer software A variety of statistical approaches have been used to find the directed dependencies among a set of interest variables and to identify the associated important factors. Among the most popular methods are proportional hazard regression and logistic regression. The aim of the current study is to suggest another approach by using a multilayer feed-forward neural network model (MLFF). Using body mass index (BMI) as the dependent variable, we identify its related and appropriate independent variables. In this study we put forth two MLFF models. Model 1 is where all the independent variables as identified in the literature are included, while Model 2 is where only variables found significance as a result from a multiple linear regression (MLR) analysis are included in the model. Analyses were done by using SPSS and MATLAB packages. As a result of the study, we found that the best MLFF model was the model which considered the input variables based on selection criteria for regression. International Digital Organization for Scientific Information (I D O S I) 2011 Article PeerReviewed Mohamed, Norizan and W. Ahmad, W. M. A. and Aleng, Nor Azlida and Ahmad, Maizah Hura (2011) Assessing the efficiency of multilayer feed-forward neural network model: application to body mass index data. World Applied Sciences Journal, 15 (5). pp. 677-682. ISSN 1818-4952 |
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A variety of statistical approaches have been used to find the directed dependencies among a set of interest variables and to identify the associated important factors. Among the most popular methods are proportional hazard regression and logistic regression. The aim of the current study is to suggest another approach by using a multilayer feed-forward neural network model (MLFF). Using body mass index (BMI) as the dependent variable, we identify its related and appropriate independent variables. In this study we put forth two MLFF models. Model 1 is where all the independent variables as identified in the literature are included, while Model 2 is where only variables found significance as a result from a multiple linear regression (MLR) analysis are included in the model. Analyses were done by using SPSS and MATLAB packages. As a result of the study, we found that the best MLFF model was the model which considered the input variables based on selection criteria for regression. |
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Article |
author |
Mohamed, Norizan W. Ahmad, W. M. A. Aleng, Nor Azlida Ahmad, Maizah Hura |
author_facet |
Mohamed, Norizan W. Ahmad, W. M. A. Aleng, Nor Azlida Ahmad, Maizah Hura |
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Mohamed, Norizan |
title |
Assessing the efficiency of multilayer feed-forward neural network model: application to body mass index data |
title_short |
Assessing the efficiency of multilayer feed-forward neural network model: application to body mass index data |
title_full |
Assessing the efficiency of multilayer feed-forward neural network model: application to body mass index data |
title_fullStr |
Assessing the efficiency of multilayer feed-forward neural network model: application to body mass index data |
title_full_unstemmed |
Assessing the efficiency of multilayer feed-forward neural network model: application to body mass index data |
title_sort |
assessing the efficiency of multilayer feed-forward neural network model: application to body mass index data |
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International Digital Organization for Scientific Information (I D O S I) |
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2011 |
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http://eprints.utm.my/id/eprint/44749/ |
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