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Entropy 2014, 16(9), 4788-4800; doi:10.3390/e16094788

Application of Entropy-Based Attribute Reduction and an Artificial Neural Network in Medicine: A Case Study of Estimating Medical Care Costs Associated with Myocardial Infarction

1,2
,
1,2
and
1,2,*
1
School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 6 July 2014 / Revised: 19 August 2014 / Accepted: 25 August 2014 / Published: 29 August 2014
(This article belongs to the Section Complexity)
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Abstract

In medicine, artificial neural networks (ANN) have been extensively applied in many fields to model the nonlinear relationship of multivariate data. Due to the difficulty of selecting input variables, attribute reduction techniques were widely used to reduce data to get a smaller set of attributes. However, to compute reductions from heterogeneous data, a discretizing algorithm was often introduced in dimensionality reduction methods, which may cause information loss. In this study, we developed an integrated method for estimating the medical care costs, obtained from 798 cases, associated with myocardial infarction disease. The subset of attributes was selected as the input variables of ANN by using an entropy-based information measure, fuzzy information entropy, which can deal with both categorical attributes and numerical attributes without discretization. Then, we applied a correction for the Akaike information criterion (ΑICc) to compare the networks. The results revealed that fuzzy information entropy was capable of selecting input variables from heterogeneous data for ANN, and the proposed procedure of this study provided a reasonable estimation of medical care costs, which can be adopted in other fields of medical science. View Full-Text
Keywords: artificial neural network; fuzzy information entropy; medical costs estimation; myocardial infarction disease; attribute reduction artificial neural network; fuzzy information entropy; medical costs estimation; myocardial infarction disease; attribute reduction
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Du, Q.; Nie, K.; Wang, Z. Application of Entropy-Based Attribute Reduction and an Artificial Neural Network in Medicine: A Case Study of Estimating Medical Care Costs Associated with Myocardial Infarction. Entropy 2014, 16, 4788-4800.

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