ABSTRACT
In the contemporary healthcare frameworks, Deep Learning (DL) observation is popular and suitable for prevalent exploitation of identification of diseases. This manuscript recommended an Autoencoder(AE)-based system for Chronic Kidney Disease (CKD) recognition unique proof, which can support Deep Neural Network (DNN) and operate to gain finest clarification on identification of CKD. The Principal Component Analysis (PCA) data reduction technique with a different number of components was used. Such comparison is tested against classification algorithm, namely Stacked Auto Encoder Deep Neural Network (SAEDNN). In addition, a tentative study on the exhibition of these methodologies has been carried out, in which CKD is included. Finally, an innovative tendency of PCA-SAEDNN-based classification model is proposed.
