Future Prediction of Diabetics using XG Booster Classifiers
Iyapparaja M1, Manivannan S.S2, Vinoth Kumar M, Thanapal P3, Kamalakannan J4
1Iyapparaja M, Associate Professor, SITE, Vellore Institute of Technology, Vellore, Tamilnadu, INDIA.
2Manivannan S.S, Associate Professor, SITE, Vellore Institute of Technology, Vellore, Tamilnadu, INDIA.
3Vinoth Kumar M, Associate professor at the Faculty of Information Science and Engineering, Dayananda sagar Academy of Technology and Management, Bangalore, Karnataka, INDIA.
4Thanapal P, Associate Professor, SITE, Vellore Institute of Technology, Vellore, Tamilnadu, INDIA.
5Kamalakannan J, Associate Professor, SITE, Vellore Institute of Technology, Vellore, Tamilnadu, INDIA.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2128-2132 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5144029320/2020©BEIESP | DOI: 10.35940/ijeat.C5144.029320
Open Access | Ethics and Policies | Cite | Mendeley
© 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 a most common disease that occurs to most of the humans now a day. The predictions for this disease are proposed through machine learning techniques. Through this method the risk factors of this disease are identified and can be prevented from increasing. Early prediction in such disease can be controlled and save human’s life. For the early predictions of this disease we collect data set having 8 attributes diabetic of 200 patients. The patients’ sugar level in the body is tested by the features of patient’s glucose content in the body and according to the age. The main Machine learning algorithms are Support vector machine (SVM), naive bayes (NB), K nearest neighbor (KNN) and Decision Tree (DT). In the exiting the Naive Bayes the accuracy levels are 66% but in the Decision tree the accuracy levels are 70 to 71%. The accuracy levels of the patients are not proper in range. But in XG boost classifiers even after the Naïve Bayes 74 Percentage and in Decision tree the accuracy levels are 89 to 90%. In the proposed system the accuracy ranges are shown properly and this is only used mostly. A dataset of 729 patients can be stored in Mongo DB and in that 129 patients repots are taken for the prediction purpose and the remaining are used for training. The training datasets are used for the prediction purposes.
Keywords: Diabetes mellitus, machine learning, Decision Tree prediction.