Classification Rule Discovery for Diabetes Patients by using Genetic Programming
Raj Kumar1, Rajesh Verma2
1Raj Kumar, Assitant Professor, Deptt. of Computer Sc. & Engg., Jind Institute of Engg. & Technology,Jind (Haryana), India.
2Dr. Rajesh Verma, Professor & Head, Deptt. of CSE, Kurukshetra Institute of Technology & Management, Kurukshetra (Haryana), India.
Manuscript received on September 01, 2012. | Revised Manuscript received on September 02, 2012. | Manuscript published on September 05, 2012. | PP: 183-185 | Volume-2 Issue-4, September 2012. | Retrieval Number: D0934082412/2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: 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: The learning algorithms have great application in knowledge discovery. Learning algorithm offers new beneficiary ways in for real-world applications. Genetic Programming (GP) have some advantages due to which it become suitable for classification in data mining for Knowledge Discovery(KDD) This paper focuses on the classification by using the Genetic Programming. There are various types of the traditional classification techniques like Naïve Bayesian, ID3, C4.5, CART, kNN, k-mean, SVM etc. The proposed algorithm is implemented on the Diabetes data set and excremental results are compared with traditional approach
Keywords: Classification, DM, GP, KDD,