Multilayer feedforward networks are universal approximators
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Sensitivity Analysis for Neural Networks
Sensitivity Analysis for Neural Networks
Machine Learning Approaches to Bioinformatics
Machine Learning Approaches to Bioinformatics
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis
Computers in Biology and Medicine
Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data
Artificial Intelligence in Medicine
A maximum-margin genetic algorithm for misclassification cost minimizing feature selection problem
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Hepatic fibrosis represents the principal pointer to the development of liver diseases. The correct evaluation of its degree, based on both recent non-invasive procedures and machine learning models, is of current major concern. One of the latest medical imaging methodologies for assessing it is the Fibroscan, supported by biochemical and clinical examinations. Since the complex interaction between the Fibroscan stiffness indicator and the biochemical and clinical results is hard to be manually managed towards the liver fibrosis stadialization, well-performing machine learning algorithms have been proposed to support an automatic diagnosis. We propose in this paper a tandem feature selection mechanism and evolutionary-driven neural network as a computer-based support for liver fibrosis stadialization in chronic hepatitis C. A synergetic system, based on both specific statistical tools and the sensitivity analysis provided by neural networks is used for reducing the dimension of the database from twenty-five to just six attributes. An evolutionary-trained neural network is developed afterwards for the classification of the liver fibrosis stages. The tandem approach is direct and simple, resulting from embedding the feature selection system into the method structure, in order to dynamically concentrate the search only on the most relevant attributes. Experimental results and a thorough statistical analysis clearly demonstrated the efficiency of the proposed intelligent system in comparison with other machine learning techniques reported in literature.