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.