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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
A random forest classifier for lymph diseases
Computer Methods and Programs in Biomedicine, Volume 113, No. 2, Year 2014
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Description
Machine learning-based classification techniques provide support for the decision-making process in many areas of health care, including diagnosis, prognosis, screening, etc. Feature selection (FS) is expected to improve classification performance, particularly in situations characterized by the high data dimensionality problem caused by relatively few training examples compared to a large number of measured features. In this paper, a random forest classifier (RFC) approach is proposed to diagnose lymph diseases. Focusing on feature selection, the first stage of the proposed system aims at constructing diverse feature selection algorithms such as genetic algorithm (GA), Principal Component Analysis (PCA), Relief-F, Fisher, Sequential Forward Floating Search (SFFS) and the Sequential Backward Floating Search (SBFS) for reducing the dimension of lymph diseases dataset. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the RFC for efficient classification. It was observed that GA-RFC achieved the highest classification accuracy of 92.2%. The dimension of input feature space is reduced from eighteen to six features by using GA. © 2013 Elsevier Ireland Ltd.
Authors & Co-Authors
Azar, Ahmad Taher
Egypt, Benha
Faculty of Computers and Information
Elshazly, Hanaa Ismail
Egypt, Giza
Faculty of Computers and Artificial Intelligence
Egypt, Giza
Scientific Research Group in Egypt Srge
Hassanien, Aboul Ella
Egypt, Giza
Faculty of Computers and Artificial Intelligence
Egypt, Giza
Scientific Research Group in Egypt Srge
El-Korany, Abeer Mohamed
Egypt, Giza
Faculty of Computers and Artificial Intelligence
Statistics
Citations: 160
Authors: 4
Affiliations: 3
Identifiers
Doi:
10.1016/j.cmpb.2013.11.004
ISSN:
01692607
e-ISSN:
18727565
Research Areas
Genetics And Genomics