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.
Highlights
Understanding which factors influence individual body weight and how exactly excess body fat is contributing to increase risk for disease may help to reduce the increased prevalence of several common disorders associated with obesity, thereby lessening the burden placed on health care systems
One of the most appealing features of nonparametric estimation techniques is that, by allowing the data to model the relationships among variables, they are robust to functional form specification and have the ability to detect structure which sometimes remains undetected by traditional parametric estimation techniques
Feed forward neural network utilizes the nnet package while Kernel regression estimate was done using the add-on package “np” for nonparametric regression and nonparametric specification tests
Summary
Body Mass Index (BMI) is used as a measure of persons' fitness and is considered a very important measure by health professionals. Understanding which factors influence individual body weight and how exactly excess body fat is contributing to increase risk for disease may help to reduce the increased prevalence of several common disorders associated with obesity, thereby lessening the burden placed on health care systems. BMI is termed as an indirect measure of body fat and it indicates weight-for-height without considering differences in body composition and the contribution of body fat to overall body weight. A feed forward network is an artificial neural network where connections between the units do not form a directed cycle. The feed forward neural network was the first and simplest type of artificial neural network devised.
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