Body mass index is a measure of body fitness and is considered very important in screening body categories that may lead to health problems. Understanding risk factors of obesity provide more insight and nature of policies that can be put up to fight obesity. However, uncertainty regarding most appropriate means by which to define excess body weight remains. It is important to develop models that best calculate Body Mass Index to help reduce the chances of obesity. The objective of this research ismodeling Body Mass Index using Feed Forward Neural Network and Kernel regression. Modeling will be first done using height and weight alone, later 21 body dimensions will be added. The analysis was based on body dimensions data provided by San Jose State University and the U.S. Naval Postgraduate School in Monterey, California. To determine the best model, Adjusted R2 and Mean Square Error (MSE) were used. From the results of the study, Kernel regression was better in modeling Body Mass Index than Feed Forward Neural Network.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 5, Issue 4) |
| DOI | 10.11648/j.ajtas.20160504.13 |
| Page(s) | 180-185 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Feed Forward Neural Network, Body Mass Index (BMI), Artificial Neural Network (ANN), Kernel Regression
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APA Style
Nzinga Christine Mutono, Gichuhi Anthony Waititu, Wanjoya Anthony Kiberia. (2016). Feed Forward Neural Network Versus Kernel Regression a Case of Body Mass Index and Body Dimensions. American Journal of Theoretical and Applied Statistics, 5(4), 180-185. https://doi.org/10.11648/j.ajtas.20160504.13
ACS Style
Nzinga Christine Mutono; Gichuhi Anthony Waititu; Wanjoya Anthony Kiberia. Feed Forward Neural Network Versus Kernel Regression a Case of Body Mass Index and Body Dimensions. Am. J. Theor. Appl. Stat. 2016, 5(4), 180-185. doi: 10.11648/j.ajtas.20160504.13
AMA Style
Nzinga Christine Mutono, Gichuhi Anthony Waititu, Wanjoya Anthony Kiberia. Feed Forward Neural Network Versus Kernel Regression a Case of Body Mass Index and Body Dimensions. Am J Theor Appl Stat. 2016;5(4):180-185. doi: 10.11648/j.ajtas.20160504.13
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Download@article{10.11648/j.ajtas.20160504.13,
author = {Nzinga Christine Mutono and Gichuhi Anthony Waititu and Wanjoya Anthony Kiberia},
title = {Feed Forward Neural Network Versus Kernel Regression a Case of Body Mass Index and Body Dimensions},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {5},
number = {4},
pages = {180-185},
doi = {10.11648/j.ajtas.20160504.13},
url = {https://doi.org/10.11648/j.ajtas.20160504.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20160504.13},
abstract = {Body mass index is a measure of body fitness and is considered very important in screening body categories that may lead to health problems. Understanding risk factors of obesity provide more insight and nature of policies that can be put up to fight obesity. However, uncertainty regarding most appropriate means by which to define excess body weight remains. It is important to develop models that best calculate Body Mass Index to help reduce the chances of obesity. The objective of this research ismodeling Body Mass Index using Feed Forward Neural Network and Kernel regression. Modeling will be first done using height and weight alone, later 21 body dimensions will be added. The analysis was based on body dimensions data provided by San Jose State University and the U.S. Naval Postgraduate School in Monterey, California. To determine the best model, Adjusted R2 and Mean Square Error (MSE) were used. From the results of the study, Kernel regression was better in modeling Body Mass Index than Feed Forward Neural Network.},
year = {2016}
}
TY - JOUR T1 - Feed Forward Neural Network Versus Kernel Regression a Case of Body Mass Index and Body Dimensions AU - Nzinga Christine Mutono AU - Gichuhi Anthony Waititu AU - Wanjoya Anthony Kiberia Y1 - 2016/06/07 PY - 2016 N1 - https://doi.org/10.11648/j.ajtas.20160504.13 DO - 10.11648/j.ajtas.20160504.13 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 180 EP - 185 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20160504.13 AB - Body mass index is a measure of body fitness and is considered very important in screening body categories that may lead to health problems. Understanding risk factors of obesity provide more insight and nature of policies that can be put up to fight obesity. However, uncertainty regarding most appropriate means by which to define excess body weight remains. It is important to develop models that best calculate Body Mass Index to help reduce the chances of obesity. The objective of this research ismodeling Body Mass Index using Feed Forward Neural Network and Kernel regression. Modeling will be first done using height and weight alone, later 21 body dimensions will be added. The analysis was based on body dimensions data provided by San Jose State University and the U.S. Naval Postgraduate School in Monterey, California. To determine the best model, Adjusted R2 and Mean Square Error (MSE) were used. From the results of the study, Kernel regression was better in modeling Body Mass Index than Feed Forward Neural Network. VL - 5 IS - 4 ER -
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