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A random forest classifier for lymph diseases
Authors:Ahmad Taher Azar  Hanaa Ismail Elshazly  Aboul Ella Hassanien  Abeer Mohamed Elkorany
Affiliation:1. Faculty of Computers and Information, Benha University, Egypt;2. Faculty of Computers and Information, Cairo University, Egypt;3. Scientific Research Group in Egypt (SRGE), Egypt
Abstract: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.
Keywords:Machine learning (ML)  Feature selection (FS)  Genetic algorithm (GA)  Random forest classifier (RFC)  Lymph diseases
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