Abstract
ResearchThe body composition model is closely related to the physiological characteristics of the human body. At the same time there can be a large number of physiological characteristics, many of which may be redundant or irrelevant. In existing human physiological feature selection algorithms, it is difficult to overcome the impact that redundancy and irrelevancy may have on human body composition modeling. This suggests a role for selection algorithms, where human physiological characteristics are identified using a combination of filtering and improved clustering. To do this, a feature filtering method based on Hilbert-Schmidt dependency criteria is first of all used to eliminate irrelevant features. After this, it is possible to use improved Chameleon clustering to increase the combination of sub-clusters amongst the characteristics, thereby removing any redundant features to obtain a candidate feature set for human body composition modeling. MethodWe report here on the use of an algorithm to filter the characteristic parameters in INBODY770 (this paper used INBODY 770 as body composition analyzer.) measurement data, which has three commonly-used impedance bands (1 kHZ, 250 kHZ, 500 kHZ). This algorithm is able to filter out parameters that have a low correlation with body composition BFM. The algorithm is also able to draw upon improved clustering techniques to reduce the initial feature set from 29 parameters to 10 parameters for any parameters of the 250 kHZ band that remain after filtering. In addition, we also examined the impact of different sample sizes on feature selection.ResultThe proposed algorithm is able to remove irrelevant and redundant features and the resulting correlation between the model and the body composition (BFM which is a whole body fat evaluation can better assess the body's overall fat and muscle composition.) is 0.978, thereby providing an improved model for prediction with a relative error of less than 0.12.
Highlights
To a certain extent, changes in the body composition reflect the change of the physical health status
In view of that the current human body composition model is constructed in the experience prediction model of body composition based on the various bioelectrical impedance measurement method (BIA), and the INBODY770 will be used as as body composition analyzer and a source of data in this paper.[1]
Human body physiological feature selection algorithm remove features that are not related to the class
Summary
Changes in the body composition reflect the change of the physical health status. Changes in human body composition tend to appear earlier than the clinical symptoms of the disease [3]. There is a need to reduce the amount of high-dimensional data present in human body composition parameters. Clustering methods can divide body composition parameter data into several groups or clusters so that intracluster objects have high degrees of similarity [10], thereby effectively sifting out redundant features according to the distance between each cluster and a central point. It is important to reduce the number of features and eliminate any properties that are not relevant before high-dimensional data analysis of a body’s composition can take place
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