Open AccessOptimal Deep Learning based Data Classification Model for Type-2 Diabetes Mellitus Diagnosis and Prediction System
Author(s) -
M. Ganesan,
N. Sivakumar,
M. Thirumaran,
R. Saravanan
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c8656.019320
Subject(s) - computer science , artificial intelligence , classifier (uml) , artificial neural network , machine learning , deep learning , multilayer perceptron , deep neural networks , data mining , pattern recognition (psychology)
In recent days, deep learning models become a significant research area because of its applicability in diverse domains. In this paper, we employ an optimal deep neural network (DNN) based model for classifying diabetes disease. The DNN is employed for diagnosing the patient diseases effectively with better performance. To further improve the classifier efficiency, multilayer perceptron (MLP) is employed to remove the misclassified instance in the dataset. Then, the processed data is again provided as input to the DNN based classification model. The use of MLP significantly helps to remove the misclassified instances. The presented optimal data classification model is experimented on the PIMA Indians Diabetes dataset which holds the medical details of 768 patients under the presence of 8 attributes for every record. The obtained simulation results verified the superior nature of the presented model over the compared methods.