Application of Artificial Neural Networks and Rough Set Theory for the Analysis of Various Medical Problems and Nephritis Disease Diagnosis

2014 ◽  
pp. 83-90 ◽  
Author(s):  
Devashri Raich ◽  
P. S. Kulkarni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Disease Diagnosis ◽  
Medical Problems ◽  
Artificial Neural

Applying Rough Set Theory to Establish Artificial Neural Networks Model for Short Term Incidence Rate Forecasting

2013 ◽  
Vol 347-350 ◽  
pp. 2937-2941 ◽  
Author(s):  
Xiang Yu Zhao ◽  
Liang Liang Ma
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Incidence Rate ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Forecast Model ◽  
Short Term ◽  
Artificial Neural ◽  
Input Variables

Choosing input variable and networks architecture are key processes for modeling short term incidence rate forecast by artificial neural networks, in this paper a method based on rough set theory is proposed to deal with them. In the proposed approach, the key factors that affect the incidence rate forecasting are firstly identified by rough set theory and then the input variables of forecast model can be determined. On the basis of the process mentioned above a set of influence rules can been obtained through reductive mining process of attributes and attribute values, then a neural networks of incidence rate forecast model is established on the rule set and BP-algorithm is adopt to optimize the networks. The method indicates that incidence rate forecast model can be established according some theoretical principles and avoiding blindness. A practical application is given at last to demonstrate the usefulness of the novel method.

scholarly journals The Impact of Macroeconomic Factors on Residential Property Price Indices in Europe

2012 ◽  
Vol 12 (2) ◽  
pp. 103-125 ◽  
Author(s):  
Małgorzata Renigier-Biłozor ◽  
Radosław Wiśniewski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
European Countries ◽  
Residential Property ◽  
Price Indices ◽  
Property Price ◽  
Property Markets

Abstract This paper aims to determine the influence of selected variables on residential property price indices for the European countries, with particular attention paid to Italy and Poland, using a rough set theory and an approach that uses a committee of artificial neural networks. Additionally, the overall analysis for each European country is presented. Quarterly time series data constituted the material for testing and empirical results. The developed models show that the economic and financial situation of European countries affects residential property markets. Residential property markets are connected, despite the fact that they are situated in different parts of Europe. The economic and financial crisis of countries has variable influence on prices of real estate. The results also suggest that methodology based on the rough set theory and a committee of artificial neural networks has the ability to learn, generalize, and converge the residential property prices index.

Diagnostic Cost Reduction Using Artificial Neural Networks

2011 ◽  
pp. 1812-1830
Author(s):  
Steven Walczak ◽  
Bradley B. Brimhall ◽  
Jerry B. Lefkowitz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Laboratory ◽  
Predictive Performance ◽  
Diagnostic Model ◽  
Medical Problems ◽  
Ann Models ◽  
Artificial Neural ◽  
Diagnostic Capabilities ◽  
Performance Results

Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly to diagnose with respect to direct costs, including pulmonary embolism (PE). Advanced decision-making tools such as artificial neural networks (ANNs) improve diagnostic capabilities for these problematic medical conditions. The research in this chapter develops a backpropagation trained ANN diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for a PE, with 15% suffering a confirmed PE, are collected and used to evaluate various ANN models’ performance. Results indicate that using ANN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, significantly improving both overall positive predictive and negative predictive performance.

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