Optimal Deep Learning based Data Classification Model for Type-2 Diabetes Mellitus Diagnosis and Prediction System
M. Ganesan1, N. Sivakumar2, M. Thirumaran3, R. Saravanan4
1M. Ganesan, Research Scholar, Dept. of CSE, Pondicherry University, Puducherry, India.
2N. Sivakumar, Assistant Professor, Dept. of CSE, Pondicherry Engineering College, Puducherry, India.
3M. Thirumaran, Assistant Professor, Dept. of CSE, Pondicherry Engineering College, Puducherry, India.
4R. Saravanan Assistant Professor, Dept. of IT, Sri Manakula Vinayagar Engineering College, Puducherry, India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 1596-1604 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8656019320/2020©BEIESP | DOI: 10.35940/ijitee.C8656.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Classification, Medical Data, Deep Learning, Multilayer Perceptron.
Scope of the Article: Classification