Feed Forward Neural Network Versus Kernel Regression a Case of Body Mass Index and Body Dimensions
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 4, July 2016, Pages: 180-185
Received: May 5, 2016;
Accepted: May 18, 2016;
Published: Jun. 7, 2016
Views 4407 Downloads 142
Nzinga Christine Mutono, Applied Statistics, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Gichuhi Anthony Waititu, Statistics, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Wanjoya Anthony Kiberia, Statistics, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Follow on us
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.
Feed Forward Neural Network, Body Mass Index (BMI), Artificial Neural Network (ANN), Kernel Regression
To cite this article
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, American Journal of Theoretical and Applied Statistics.
Vol. 5, No. 4,
2016, pp. 180-185.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cizek, P and W Hardle (2006), ‘Robust estimation of dimension reduction space’, ComputationalStatistics and Data Analysis 51, 545–555.
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’.
Heinz, G, L J Peterson, R W Johnson and Kerk C J (2003), ‘Exploring relationships in bodydimensions’, Journal of Statistics Education 11, 1–15.
Ho, S Y, T H Lam and E D Janus (2003), ‘The hongkong cardiovascular risk factor prevalencestudy steering committee’, Ann Epidemiolpp. 683–691
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.
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.
Kvaavik, E, GS Tell and K Klepp (2003), ‘Predictors and tracking of body mass index fromadolescence into adulthood’, Arch PediatrAdolesc Med 12, 1212–1218.
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.
Nadaraya, E A (1964), ‘On parametric estimates of density function and regresssion curves’, Theory of Applied Probabilityl 10, 186–190.37
Rosenblatt, M (1956), ‘Remarks on some nonparametric estimates of a density function’, TheAnnals of Mathematical Statistics 27, 832–837.
Stone, C J (1977), ‘Consistent nonparametric regression’, Annals of Statistics 5, 595–645.
Wei, M, S P Gaskill, S M Haffner and M P Stern (1997), Waist circumference as a predictor ofdiabetes, 5 edn, Obes Res.
Welborn, T A and S SDhaliwal (2007), ‘Preferred clinical measures of central obesity forpredicting mortality’, Eur J ClinNutrpp. 1373–1379.