An Efficient Ada Max based Parameter Tuned Deep Neural Network for Medical Data Classification

Abstract

Medical data classification involves the application of intelligent algorithms to examine the medical dataset for the detection of diseases. This paper concentrates on a medical data classification process to determine the existence of particular diseases for diagnostics and prognostics. The proposed model uses an AdaMax based deep neural network (DNN) model, called AM-DNN for medical data classification. The presented AM-DNN model comprises different processes namely preprocessing, classification, and parameter optimization. The presented model preprocesses the medical data in the initial phase to transform it into a compatible format. In addition, DNN based classification process gets executed to allocate the proper class label of DNN. Besides, AM optimizer is applied to fine-tune the parameters of DNN model. The application of AM helps to improvise the efficiency of the DNN model. For assessing the simulation performance of the AM-DNN model, a series of experiments were performed. The obtained outcomemakes sure that the AM-DNN model has resulted in a maximum accuracy of 0.9275, 0.8945, and 0.9333 on the applied chronic kidney disease (CKD), diabetes, and heart disease datasets.