Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Hybrid system based on rough sets and genetic algorithms for medical data classifications
International Journal of Fuzzy System Applications, Volume 3, No. 4, Year 2013
Notification
URL copied to clipboard!
Description
Computational intelligence provides the biomedical domain by a significant support. The application of machine learning techniques in medical applications have been evolved from the physician needs. Screening, medical images, pattern classification, prognosis are some examples of health care support systems. Typically medical data has its own characteristics such as huge size and features, continuous and real attributes that refer to patients' investigations. Therefore, discretization and feature selection process are considered a key issue in improving the extracted knowledge from patients' investigations records. In this paper, a hybrid system that integrates Rough Set (RS) and Genetic Algorithm (GA) is presented for the efficient classification of medical data sets of different sizes and dimensionalities. Genetic Algorithm is applied with the aim of reducing the dimension of medical datasets and RS decision rules were used for efficient classification. Furthermore, the proposed system applies the Entropy Gain Information (EI) for discretization process. Four biomedical data sets are tested by the proposed system (EI-GA-RS), and the highest score was obtained through three different datasets. Other different hybrid techniques shared the proposed technique the highest accuracy but the proposed system preserves its place as one of the highest results systems four three different sets. EI as discretization technique also is a common part for the best results in the mentioned datasets while RS as an evaluator realized the best results in three different data sets. Copyright © 2013, IGI Global.
Authors & Co-Authors
Elshazly, Hanaa Ismail
Egypt, Giza
Faculty of Computers and Artificial Intelligence
Azar, Ahmad Taher
Egypt, Benha
Faculty of Computers and Information
Hassanien, Aboul Ella
Egypt, Giza
Faculty of Computers and Artificial Intelligence
El-Korany, Abeer Mohamed
Egypt, Giza
Faculty of Computers and Artificial Intelligence
Statistics
Citations: 30
Authors: 4
Affiliations: 2
Identifiers
Doi:
10.4018/ijfsa.2013100103
ISSN:
2156177X
e-ISSN:
21561761
Research Areas
Genetics And Genomics