Diabetes prediction using Data mining Classification Techniques
M. Manjusree1, K.A. Sateesh Kumar2

1Manjusree M, School of Computer Science and Applications, REVA University, Bangalore, India.
2K.A . Sateesh Kumar, Subject Matter Expert, Merittrac Pvt. Ltd, Bangalore, India.

Manuscript received on 12 August 2019. | Revised Manuscript received on 17 August 2019. | Manuscript published on 30 September 2019. | PP: 5901-5905 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4735098319/19©BEIESP | DOI: 10.35940/ijrte.C4735.098319
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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 one of the second largest disease in the world. In the recent survey it shows that there are overall 246 million people affected with this and in that women ratio is more. By the report of WHO, this figure is going to reach to 380 million by 2025. According to the American Diabetes Association,6% of the population are not aware that there are victims of diabetes and also every 21 sec at least for an individual diabetic test result is positive. With the technology advancement in the field of medical information, data is well maintained in the databases. This paper focuses on to diagnose data to provide the solution by observing the patterns in the data using various datamining classification techniques such as Naïve basis, Logistic regression, Decision tress etc.
Index Terms: Diabetes, Data mining, WEKA, Classification Algorithms.

Scope of the Article: Classification