Aim:
Recently, classification of medical data gives more importance to identify the existence of disease.
Background:
Numerous classification algorithms for chronic kidney disease (CKD) are developed and produced better
classification results. But, the inclusion of different factors in the identification of CKD reduces the effectiveness of the
employed classification algorithm.
Objective:
To overcome this issue, feature selection (FS) approaches are proposed to minimize the computational
complexity and also to improve the classification performance in the identification of CKD. Since numerous bio-inspired
based FS methodologies are developed, a need arises to examine the feature selection approaches performance of different
algorithms on the identification of CKD.
Method:
This paper proposes a new framework for classification and prediction of CKD. Three feature selection
approaches are used namely Ant Colony Optimization (ACO) algorithm, Genetic Algorithm (GA) and Particle Swarm
Optimization (PSO) in the classification process of CKD. Finally, logistic regression (LR) classifier is employed for
effective classification.
Results:
The effectiveness of the ACO-FS, GA-FS and PSO-FS are validated by testing it against a benchmark CKD
dataset.
Conclusion:
The empirical results state that the ACO-FS algorithm performs well and the results reported that the
classification performance is improved by the inclusion of feature selection methodologies in CKD classification.
Entropy Based Feature Selection for Chronic Kidney Disease Classification