Dilip Kumar Choubey

Work place: Birla Institute of Technology, Computer Science & Engineering, Mesra, Ranchi, India

E-mail: dilipchoubey_1988@yahoo.in


Research Interests: Bioinformatics, Computer Architecture and Organization, Data Mining, Database Management System


Dilip Kumar Choubey, received  his M.Tech in Computer Science and Engineering from Oriental College of Technology (O.C.T), Bhopal, India and B.E. in Information Technology from Bansal Institute of Science and Technology (B.I.S.T), Bhopal, India. Currently, He is Persuing PhD from Birla Institue of Technology (B.I.T), Mesra, Ranchi, India. He worked as an Asst. Prof. in Lakshmi Narain College of Technology (L.N.C.T), Bhopal, India and Oriental College of Technology (O.C.T), Bhopal, India. He has 4 years of teaching and research experience. His research interests include soft computing, Bioinformatics, Data Mining and warehousing and Database Management System, etc. He has 6 International and 1 national publications.  Ph. No. 7033789676, Email Id: dilipchoubey_1988@yahoo.in

Author Articles
GA_MLP NN: A Hybrid Intelligent System for Diabetes Disease Diagnosis

By Dilip Kumar Choubey Sanchita Paul

DOI: https://doi.org/10.5815/ijisa.2016.01.06, Pub. Date: 8 Jan. 2016

Diabetes is a condition in which the amount of sugar in the blood is higher than normal. Classification systems have been widely used in medical domain to explore patient’s data and extract a predictive model or set of rules. The prime objective of this research work is to facilitate a better diagnosis (classification) of diabetes disease. There are already several methodology which have been implemented on classification for the diabetes disease. The proposed methodology implemented work in 2 stages: (a) In the first stage Genetic Algorithm (GA) has been used as a feature selection on Pima Indian Diabetes Dataset. (b) In the second stage, Multilayer Perceptron Neural Network (MLP NN) has been used for the classification on the selected feature. GA is noted to reduce not only the cost and computation time of the diagnostic process, but the proposed approach also improved the accuracy of classification. The experimental results obtained classification accuracy (79.1304%) and ROC (0.842) show that GA and MLP NN can be successfully used for the diagnosing of diabetes disease.

[...] Read more.
Other Articles