Diabetes Mellitus Prediction using Classification Techniques
Abdulhakim Salum Hassan1, I. Malaserene2, A. Anny Leema3
1Abdulhakim Salum Hassan, Department of Information Technology & Engineering Vellore Institute of Technology (VIT) Tamil Nadu, India.
2I. Malaserene, Department of Information Technology & Engineering Vellore Institute of Technology (VIT) Tamil Nadu, India.
3A. Anny Leema, Department of Information Technology & Engineering Vellore Institute of Technology (VIT) Tamil Nadu, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 27, 2020. | Manuscript published on March 10, 2020. | PP: 2080-2084 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2692039520/2020©BEIESP | DOI: 10.35940/ijitee.E2692.039520
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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 a metabolic disease affecting people in almost every country and it may lead to severe problems like stroke, kidney failure or premature death if it is not predicted at the early stage. To mitigate this many researchers are working to predict the diabetes at early stage using several methods. Different accessible conventional techniques are carried out to diagnose diabetes depend on physical and substance tests. Several data mining methods were designed to overcome these uncertainties. Classification techniques like Decision Tree, K-Nearest Neighbors, and Support Vector Machines are used to classify the patients with diabetes mellitus. The performance of these applied techniques are determined using the factors precision, accuracy, Sensitivity, and Specificity. The results obtained proved that SVM outperforms decision tree and KNN with highest accuracy of 90.23%. Performance analysis of these classification methods helps us to decide which appropriate technique to choose in future for analysing the given dataset.
Keywords: Data Mining, KNN, SVM, Decision tree, Diabetes
Scope of the Article: Data Mining