A Rough Sets/Neural Networks Approach to Knowledge Discovery for the Development of Decision Support Systems

2003 ◽  
pp. 127-148
Author(s):  
Ilona Jagielska
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Knowledge Discovery ◽  
Rough Sets ◽  
Support Systems

scholarly journals Intelligent medical systems in diagnosing and treating inflammatory diseases of the pancreas (systematic review)

2018 ◽  
Vol 39 (1) ◽  
pp. 10-15
Author(s):  
A. A. Litvin
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Inflammatory Diseases ◽  
Review Of The Literature ◽  
Medical Systems ◽  
Pancreatic Diseases

This paper is a systematic review of the literature on the use of intelligent medical systems in the diagnosis and treatment of acute inflammatory pancreatic diseases. The author provides modern literature data on the efficacy of decision support systems based on artificial neural networks to determine the severity, diagnosis and outcome prognosis of pancreatitis and complications.

Using Rough Sets for Practical Feature Selection in a Rough Sets/Neural Network Framework for Knowledge Discovery

2000 ◽  
Vol 4 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Ilona Jagielska ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Selection ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Real World ◽  
Rough Sets ◽  
Network Models ◽  
Feature Subset Selection ◽  
Neural Network Models

An important task in knowledge discovery is feature selection. This paper describes a practical approach to feature subset selection proposed as part of a hybrid rough sets/neural network framework for knowledge discovery for decision support. In this framework neural networks and rough sets are combined and used cooperatively during the system life cycle. The reason for combining rough sets with neural networks in the proposed framework is twofold. Firstly, rough sets based systems provide domain knowledge expressed in the form of If-then rules as well as tools for data analysis. Secondly, rough sets are used in this framework in the task of feature selection for neural network models. This paper examines the feature selection aspect of the framework. An empirical study that tested the approach on artificial datasets and real-world datasets was carried out. Experimental results indicate that the proposed approach can improve the performance of neural network models. The framework was also applied in the development of a real-world decision support system. The experience with this application has shown that the approach can support the users in the task of feature selection.

scholarly journals DECISION SUPPORT SYSTEMS IN THE DIAGNOSIS AND TREATMENT OF ACUTE PANCREATITIS

2016 ◽  
pp. 10-17
Author(s):  
A. A. Litvin ◽  
O. Yu. Rebrova
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Decision Making ◽  
Acute Pancreatitis ◽  
Decision Support ◽  
Decision Support Systems ◽  
Pancreatic Necrosis ◽  
Support Systems ◽  
Diagnosis And Treatment ◽  
Review Of Literature

This paper is a systematic review of literature covering the use of decision support systems in the diagnosis and treatment of acute pancreatitis. The authors provide modern literature data on the efficacy of different support systems for decision-making based on artificial neural networks to determine the severity of acute pancreatitis outcomes, prognosis and diagnosis of infected pancreatic necrosis.

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