Data Mining Technique for Diabetes Diagnosis using Classification Algorithms
Priya. M1, M. Karthikeyan2
1M. Priya, Assistant Professor, Department of Computer Science, PSPT MGR Government Arts & Science College, Sirkali – Puthur, Tamilnadu, India.
2M. Karthikeyan, Assistant Professor, Division of Computer and Information Science, Faculty of Science, Annamalai University, Annamalai Nagar, Tamilnadu, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 9044-9049 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4429118419/2019©BEIESP | DOI: 10.35940/ijrte.D4429.118419
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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 mellitus is defined as a one of the chronic and deadliest diseases which combined with abnormally high level of sugar (glucose) in the blood. The classification technique helps in diagnosis the symptoms at starting stages. This paper focused to prognosticate the chance of diabetes in patients with extremely correct classification of Diabetes. The classification algorithms viz., Naïve Bayes, Logistic Regression, and Decision Tree can be used to detect diabetes at an early stage. The algorithm performances are evaluated based on various measures like Recall, Precision, and F-Measure. Experiments are conducted where the time complexity of each of the algorithm is measured. Accuracy is also measured over correct classification and misclassification instances, observed that a Logistic Regression algorithm has much better performance when compared to the other type classifications. Using Receiver Operating Characteristic curves the results are verified in a systematic manner.
Keywords: Classification, Data Mining, Decision Tree, Diabetes Mellitus, Logistic Regression, Naïve Bayes.
Scope of the Article: Classification.