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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.