Detection of chronic kidney disease: A NN-GA-based approach

Abstract

In the present work, a genetic algorithm (GA) trained neural network (NN)-based model has been proposed to detect chronic kidney disease (CKD) which has become one of the newest threats to the developing and undeveloped countries. Studies and surveys in different parts of India have suggested that CKD is becoming a major concern day by day. The financial burden of the treatment and future consequences of CKD could be unaffordable to many, if not detected at an earlier stage. Motivated by this, the NN-GA model has been proposed which significantly overcomes the problem of using local search-based learning algorithms to train NNs. The input weight vector of the NN is gradually optimized by using GA to train the NN. The model has been compared with well-known classifiers like Random Forest, Multilayer Perception Feedforward Network (MLP-FFN), and also with NN. The performance of the classifiers has been measured in terms of accuracy, precision, recall, and F-Measure. The experimental results suggest that NN-GA-based model is capable of detecting CKD more efficiently than any other existing model.