Diagnosis of Type-2 Diabetes using Classification and Mining Techniques
Sankar Padmanabhan1, Manjunath K M2, Madhurima V3
1Sankar.P*, ECE, SV Engineering College, Karakambadi, Tirupati, AP
2Manjunath.K.M, ECE, SV Enguneering College, Karkambadi road, Tirupathi, AP.
3Madhurima. V, ECE,SV Engineering college, Karkambadi road, Tirupathi, AP .
Manuscript received on January 23, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3672-3676 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5988029320/2020©BEIESP | DOI: 10.35940/ijeat.C5988.029320
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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: Around two hundred and fifty million individuals, with a major part of them being ladies influenced by diabetes. This number may ascend to 380 million by another decade. The sickness has been named as the fifth deadliest illness in the world with not a single inevitable fix to be seen. With the ascent of data innovation and proceeding with an approach into the restorative and medicinal services part, the instances of diabetes and their side effects all around are archived. Information mining is a buzz word separating concealed data from an enormous arrangement of database. It assists scientists in building large database in the area of biomedical engineering. The Pima Indian diabetes database was used for investigation purpose. In this paper an attempt has been made to study the effect of various classification and mining Techniques like Decision Tree, Naïve Bayes, SVM, Regression etc on the diagnosis of Type-2 diabetes.
Keywords: Algorithms, Heart rate variability, J48, Regression, SVM.