Diagnosis of Diabetes by using Data Mining Techniques
Sachin Kumar1, Narender Kumar2
1Sachin Kumar*, M.Tech Degree in Department of Computer Science and Engineering from Guru Jambheshwar University of Science and Technology, Hisar, Haryana(India).
2Mr. Narender Kumar, Assistant Professor in department of Computer Science and Engineering in Guru Jambheshwar University of Science and Technology, Hisar, Haryana(India).
Manuscript received on October 15, 2019. | Revised Manuscript received on 24 October, 2019. | Manuscript published on November 10, 2019. | PP: 54-57 | Volume-9 Issue-1, November 2019. | Retrieval Number: L31741081219/2019©BEIESP | DOI: 10.35940/ijitee.L3174.119119
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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: There are many classifiers that are used for diagnosis of diabetes but the result of this paper shows that how logistic regression having best accuracy among the other classifiers. Logistic regression removes the disadvantages of linear regression. There are different classifiers that are used for prediction. In the worldwide millions of peoples are suffering from diabetes according to WHO report. In the medical region, many researches have done with the help of data mining. The aim of this paper is to diagnosis of diabetes by using the best classifiers and providing best parameter tuning. The study helps to find whether a patient is enduring from diabetes or not using classification methods and it further investigate and evaluates the functioning of different classification in relations of precision, accuracy, recall & roc.
Keywords: Diabetes, KNN, Classification, Decision tree, logistic regression, SVM.
Scope of the Article: Classification