Oppositional firefly optimization based optimal feature selection in chronic kidney disease classification using deep neural network

2021 ◽  
Author(s):  
Jerlin Rubini Lambert ◽  
Eswaran Perumal
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Deep Neural Network ◽  
Disease Classification ◽  
Firefly Optimization ◽  
Optimal Feature Selection ◽  
Optimal Feature

Optimal Feature Selection Methods for Chronic Kidney Disease Classification using Intelligent Optimization Algorithms

2020 ◽  
Vol 13 ◽  
Author(s):  
Jerlin Rubini Lambert ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Classification Performance ◽  
Disease Classification ◽  
Intelligent Optimization Algorithms ◽  
Optimal Feature Selection ◽  
Optimal Feature ◽  
New Framework

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.

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