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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics
Ali Soltani
Shiraz University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Neural Network Model of Estimation of Body Mass Index Based on Indirect Input Factors Seyed Hosein Hoseini; Meisam Pourahmadi-Nakhli; Ali Soltani
Bulletin of Electrical Engineering and Informatics Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (179.27 KB) | DOI: 10.11591/eei.v2i3.207

Abstract

A well-prepared One of the main concerns of people in developing and developed societies is increasing the Body Mass Index (BMI) level. BMI, in fact can be considered as an indicator of overall health condition. Genetic aspects aside, the BMI level is affected by different factors, such as socio-economic, environmental, and physical activity level. This study investigated the effect of different factors on the BMI level of a sample population of 470 adults of three residential neighborhoods in Shiraz, Iran. The Pearson correlation coefficient, independent sample T-test and One Way ANOVA were used to extract the variables which significantly influenced the BMI. The statistical analysis showed that despite the apparent association of BMI with physical activity level, it is influenced by several factors such as age, residence record, number of children, distance to bus or taxi stop, indoor or sport exercise. Then, an Artificial Neural Network (ANN) was applied to predict the level of personal BMI. Artificial Neural Network-based methodology results showed that the generalized estimating ANN model was satisfactory in estimating the BMI based on the introduced pattern.
Neural Network Model of Estimation of Body Mass Index Based on Indirect Input Factors Seyed Hosein Hoseini; Meisam Pourahmadi-Nakhli; Ali Soltani
Bulletin of Electrical Engineering and Informatics Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v2i3.207

Abstract

A well-prepared One of the main concerns of people in developing and developed societies is increasing the Body Mass Index (BMI) level. BMI, in fact can be considered as an indicator of overall health condition. Genetic aspects aside, the BMI level is affected by different factors, such as socio-economic, environmental, and physical activity level. This study investigated the effect of different factors on the BMI level of a sample population of 470 adults of three residential neighborhoods in Shiraz, Iran. The Pearson correlation coefficient, independent sample T-test and One Way ANOVA were used to extract the variables which significantly influenced the BMI. The statistical analysis showed that despite the apparent association of BMI with physical activity level, it is influenced by several factors such as age, residence record, number of children, distance to bus or taxi stop, indoor or sport exercise. Then, an Artificial Neural Network (ANN) was applied to predict the level of personal BMI. Artificial Neural Network-based methodology results showed that the generalized estimating ANN model was satisfactory in estimating the BMI based on the introduced pattern.
Neural Network Model of Estimation of Body Mass Index Based on Indirect Input Factors Seyed Hosein Hoseini; Meisam Pourahmadi-Nakhli; Ali Soltani
Bulletin of Electrical Engineering and Informatics Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (179.27 KB) | DOI: 10.11591/eei.v2i3.207

Abstract

A well-prepared One of the main concerns of people in developing and developed societies is increasing the Body Mass Index (BMI) level. BMI, in fact can be considered as an indicator of overall health condition. Genetic aspects aside, the BMI level is affected by different factors, such as socio-economic, environmental, and physical activity level. This study investigated the effect of different factors on the BMI level of a sample population of 470 adults of three residential neighborhoods in Shiraz, Iran. The Pearson correlation coefficient, independent sample T-test and One Way ANOVA were used to extract the variables which significantly influenced the BMI. The statistical analysis showed that despite the apparent association of BMI with physical activity level, it is influenced by several factors such as age, residence record, number of children, distance to bus or taxi stop, indoor or sport exercise. Then, an Artificial Neural Network (ANN) was applied to predict the level of personal BMI. Artificial Neural Network-based methodology results showed that the generalized estimating ANN model was satisfactory in estimating the BMI based on the introduced pattern.
Co-Authors Meisam Pourahmadi-Nakhli Seyed Hosein Hoseini