Implementing Classification Algorithms for Predicting Chronic Diabetes Diseases
M. Kavitha1, S. Subbaiah2
1M. Kavitha, Assistant Professor & Ph.D Part Time Research Scholar, PG & Research Department of Computer Science and Applications, Vivekanandha College of Arts and Science for Women, Tiruchengode (Tamil Nadu), India.
2Dr. S. Subbaiah, Assistant Professor, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
Manuscript received on 30 September 2019 | Revised Manuscript received on 12 November 2019 | Manuscript Published on 22 November 2019 | PP: 1748-1751 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F13280986S319/19©BEIESP | DOI: 10.35940/ijeat.F1328.0986S319
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Abstract: Now a day Chronic Diabetes Disease is increasing due to many reasons like changes in life style, food habit. It causes an increase in blood sugar levels. If Diabetes Disease remains untreated or unidentified, many different types of complications may be occurred. The doctors have the problem to identify these kinds of diseases easily. The machine learning algorithms helps the doctor to solve these types of problems. In this paper, we implemented three algorithms namely logistic regression, Naive Bayes and Decision tree algorithms to predict diabetes at an early stage. Experiments are performed on Pima Indians Diabetes Dataset, which is from UCI machine learning repository. The performance of all the three algorithms is evaluated using measures on Accuracy. Results obtained showed logistic regression displays 75.3%, Decision tree displays 77.9% and Naive Bayes classifier displays the accuracy value is 76.6%.
Keywords: Diabetes, Logistic Regression, Decision Tree, Naive Bayes, Machine Learning.
Scope of the Article: Classification