Type II Diabetes Prediction Using Combo of SVM
Minyechil Alehegn1, Rahul Raghvendra Joshi2

1Minyechil Alehegn*, School of Computing and Informatics, Department of IT, MizanTepi University, Tepi, Ethiopia.
2Rahul Raghvenrda Joshi, CS/IT, Symbiosis International (Deemed) University/ Symbiosis Institute of Technology, Pune, (Maharashtra) India.
Manuscript received on July 10, 2019. | Revised Manuscript received on August 05, 2019. | Manuscript published on August 30, 2019. | PP: 712-715 | Volume-8 Issue-6, August 2019. | Retrieval Number: F7974088619/2019©BEIESP | DOI: 10.35940/ijeat.F7974.088619
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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: In 21th century, IT plays a very important and helpful role in health care industries acting as a savior to human life. Data mining and machine learning are two sides of healthcare-IT. Proposed system considers one of the most common chronic diseases called diabetes. India and almost all other countries are worried about diabetic patients, so diabetes can termed as a global chronic disease. In this paper, well-known predictive machine learning techniques viz. SVM, Random Tree and ANN are applied on PIMA dataset. Results of SVM, ANN, and RT are 90.1%, 88.02%, and 83.59% respectively.
Keywords: PIMA, SVM, ANN, Random Tree etc.