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Creatinine prediction from body composition a neural network approach

by Thitipong Tanprasert; Chularat Tanprasert

Title:

Creatinine prediction from body composition a neural network approach

Author(s):

Thitipong Tanprasert
Chularat Tanprasert

Issued date:

2011

Citation:

International Journal of Innovative Management, Information & Production Volume 2, Number 1, March 2011

Abstract:

Creatinine, a naturally-produced chemical compound in blood, has been commonly used as a reliable indicator of kidney function. Creatinine level is typically obtained from blood-test. In this paper, a technique for predicting the criticality of creatinine level in blood is presented. The proposed technique takes only body size and mass parameters obtained from advanced weighing scale and body scanner, allowing the prediction to be done more casually. The technique applies a multi-layered feed-forward neural network for developing the prediction model. The achieved overall prediction accuracy is in the vicinity of 88% where the average false negative rate and the average false positive rate are 22.15% and 8.26%, respectively.

Keyword(s):

Creatinine
Prediction
Kidney
Health

Resource type:

Article

Extent:

8 pages

Type:

Text

File type:

application/pdf

Language:

eng

Rights holder(s):

Thitipong Tanprasert
Chularat Tanprasert

URI:

http://repository.au.edu/handle/6623004553/17936
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  • Articles [27]


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Copyright © Assumption University.
All Rights Reserved.

Contact Us

The St. Gabriel's Library   
Hua Mak Campus  
Ramkhamhaeng 24, Hua Mak  
Bangkok Thailand 10240  
Tel.: (662) 3004543-62 Ext. 3402  
Fax.: (662) 7191544  
E-Mail Library : library@au.edu  


The Cathedral of Learning Library
Suvarnabhumi Campus
Bang Na-Trad Km. 26 Bangsaothong
Samuthprakarn Thailand 10540
Tel.: (662) 7232024, 7232025
Fax.: (662) 7191544
E-Mail Library : library@au.edu
 

 

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