Optimal Feature Selection for Chronic Kidney Disease Classification Using Deep Learning Classifier

Chronic Kidney Disease (CKD) is an increasing failure of kidney function leading to kidney failure over the years. The disease settles down and hence makes its diagnosis difficult. Analyzing CKD stages from standard office visit records can assist in premature recognition of the disease and prompt auspicious mediation. Hereby, we propose a methodology using inspired optimization model and learning procedure to classify CKD. The proposed method selects applicable features of kidney data with the help of Ant Lion Optimization (ALO) technique to choose optimal features for the classification process. After that, we sort the CKD data based on chosen features by utilizing Deep Neural Network (DNN). Performance comparison indicates that our proposed model accomplishes better classification accuracy, precision, F-measure, sensitivity measures when compared with other data mining classifiers.