NDLI: Prediction model of human body composition based on physiological information entropy

Content Provider	IEEE Xplore Digital Library
Author	Chen, Bo Gao, Xiu-e Zhang, Aiqiang
Copyright Year	2015
Description	Author affiliation: College of Information Engineering, Dalian University, Dalian, China (Chen, Bo; Gao, Xiu-e; Zhang, Aiqiang)
Abstract	Body composition analysis can not only reflect the state of health, but also can play a role in disease prevention. Aiming at many influencing factors, complex modeling issues of the existing bioelectrical impedance analysis algorithms, this paper draws information entropy theory into modeling the human body composition for the first time, establishes entropy evaluation criteria of physiological characteristic parameters, puts forward feature selection algorithm based on physiological information entropy, selects a reasonable subset of features that can most effectively interpret body physiological information and have a minimal number of features to give the body composition prediction fitted model. Experimental results show that the algorithm can select the useful characteristic parameters and the fitted model improves the accuracy of body composition prediction.
Starting Page	495
Ending Page	499
File Size	242128
Page Count	5
File Format	PDF
e-ISBN	9781509000227
DOI	10.1109/BMEI.2015.7401555
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	body composition Biological system modeling feature selection algorithm Predictive models fitted model Physiology physiological information entropy Impedance Mathematical model Information entropy Mutual information
Content Type	Text
Resource Type	Article

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