| Peer-Reviewed

Feed Forward Neural Network Versus Kernel Regression a Case of Body Mass Index and Body Dimensions

Received: 5 May 2016     Accepted: 18 May 2016     Published: 7 June 2016
Views:       Downloads:
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.

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), 2016. Published by Science Publishing Group

Keywords

Feed Forward Neural Network, Body Mass Index (BMI), Artificial Neural Network (ANN), Kernel Regression

References
[1] Cizek, P and W Hardle (2006), ‘Robust estimation of dimension reduction space’, ComputationalStatistics and Data Analysis 51, 545–555.
[2] Frayling, Timothy M, Impson, Nicholas J and Weedon (2007), ‘A common variant in the ftogene is associated with body mass index and predisposes to childhood and adult obesity’.
[3] Heinz, G, L J Peterson, R W Johnson and Kerk C J (2003), ‘Exploring relationships in bodydimensions’, Journal of Statistics Education 11, 1–15.
[4] Ho, S Y, T H Lam and E D Janus (2003), ‘The hongkong cardiovascular risk factor prevalencestudy steering committee’, Ann Epidemiolpp. 683–691
[5] Hoseini, seyedHosein and Soltani (2012), ‘Application of artificial neural network in estimationof body mass index based on connection between enviromental factors and physical activity’, International journal of artificial inteligence and applications.
[6] Jansses, I, P T Katzmarzyk and P Ross (2004), ‘Waist circumfrence and not body mass indexexplains obesity related health risk’, Am J ClinNutrpp. 379–384.
[7] Kvaavik, E, GS Tell and K Klepp (2003), ‘Predictors and tracking of body mass index fromadolescence into adulthood’, Arch PediatrAdolesc Med 12, 1212–1218.
[8] Li, Q and J S Racine (2008), ‘Nonparametric estimation of conditional cdf and quantilefunctionswith mixed categorical and continuous data’, Journal of Business and Economic Statistics.
[9] Nadaraya, E A (1964), ‘On parametric estimates of density function and regresssion curves’, Theory of Applied Probabilityl 10, 186–190.37
[10] Rosenblatt, M (1956), ‘Remarks on some nonparametric estimates of a density function’, TheAnnals of Mathematical Statistics 27, 832–837.
[11] Stone, C J (1977), ‘Consistent nonparametric regression’, Annals of Statistics 5, 595–645.
[12] Wei, M, S P Gaskill, S M Haffner and M P Stern (1997), Waist circumference as a predictor ofdiabetes, 5 edn, Obes Res.
[13] Welborn, T A and S SDhaliwal (2007), ‘Preferred clinical measures of central obesity forpredicting mortality’, Eur J ClinNutrpp. 1373–1379.
Cite This Article
  • 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

    Copy | Download

    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

    Copy | Download

    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

    Copy | 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}
    }
    

    Copy | Download

  • 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  - 

    Copy | Download

Author Information
  • Applied Statistics, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Statistics, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Statistics, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Sections