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Kernel regression was better than Feed Forward Neural Network in modeling Body Mass Index using height, weight, and 21 body dimensions based on Adjusted R2 and Mean Square Error.
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Kernel regression outperforms Feed Forward Neural Networks in modeling Body Mass Index using body dimensions.
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.
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Nzinga Christine Mutono (2016) studied Body Mass Index / Obesity. Kernel regression vs. Feed Forward Neural Network was evaluated on Model performance evaluated by Adjusted R2 and Mean Square Error (MSE). Kernel regression was better than Feed Forward Neural Network in modeling Body Mass Index using height, weight, and 21 body dimensions based on Adjusted R2 and Mean Square Error.
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