Classification of Diabetes using Random Forest with Feature Selection Algorithm
K.Koteswara Chari1, M.Chinna babu2, Sarangarm Kodati3
1K.Koteswara Chari*, CSE Department, Teegala Krishna Reddy Engineering College, Computer Science and Engineering, Telangana, India.
2M.Chinna Babu, CSE Department, Teegala Krishna Reddy Engineering College, Computer Science and Engineering, Telangana, India.
3Sarangam Kodati, CSE Department, Teegala Krishna Reddy Engineering College, Computer Science and Engineering, Telangana, India.
Manuscript received on October 11, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 1295-1300 | Volume-9 Issue-1, November 2019. | Retrieval Number: L35951081219/2019©BEIESP | DOI: 10.35940/ijitee.L3595.119119
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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 has become a serious problem now a day. So there is a need to take serious precautions to eradicate this. To eradicate, we should know the level of occurrence. In this project we predict the level of occurrence of diabetes. We predict the level of occurrence of diabetes using Random Forest, a Machine Learning Algorithm. Using the patient’s Electronic Health Records (EHR) we can build accurate models that predict the presence of diabetes.
Keywords: Electronic Health Records, Random Forest with Feature Selection, Machine Learning Algorithm.
Scope of the Article: Machine Learning