ENN-Ensemble based Neural Network method for Diabetes Classification
G L Aruna Kumari1, Padmaja P2, Jaya Suma G3

1G L Aruna Kumari, Dept of CSEGitam institute of Technology, Gitam University Visakhapatnam, India.
2Dr Padmaja P, Professor and Head,Dept.of Information Technology Anil Neerukonda Institute of Technology &Sciences, Visakhapatnam,India.
3Dr.Jaya Suma G, Professor and Head, Department of Information Technology JNTUK-University College of Engineering ,Vizianagaram, India

Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 574-579 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C4819029320/2020©BEIESP | DOI: 10.35940/ijeat.C4819.029320
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© 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: Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENNEnsemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques.
Keywords: Diabetes classification, diabetes mellitus, neural network, ensemble learning.