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

机译:前馈神经网络与内核回归-体重指数和身体尺寸的案例

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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.
机译:体重指数是衡量身体健康状况的一项指标,被认为在筛选可能导致健康问题的身体类别中非常重要。了解肥胖的危险因素可以提供更多的见解和可以用来对抗肥胖的政策的性质。但是,仍然存在关于确定超重的最适当方法的不确定性。重要的是要开发能够最佳地计算体重指数的模型,以帮助减少肥胖的机会。这项研究的目的是使用前馈神经网络和核回归模型对体重指数进行建模。首先将仅使用身高和体重进行建模,之后将添加21个车身尺寸。该分析基于圣何塞州立大学和加利福尼亚州蒙特雷的美国海军研究生院提供的身体尺寸数据。为了确定最佳模型,使用了调整后的R2和均方误差(MSE)。从研究结果来看,在建模身体质量指数方面,内核回归优于前馈神经网络。

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