An Ontology Driven System to Predict Diabetes With Machine Learning Techniques
Divakar H R1, D Ramesh2, B R Prakash3
1Divakar H R, PES College of Engineering, Mandya, Karnataka, India.
2Dr. D Ramesh, Professor, Sri Siddartha Academy of Higher Education, Tumkur, Karnataka, India.
3Dr. B R Prakash, Govt. First Grade College, Tiptur, Karnataka, India,
Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 4005-4011 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7586129219/2019©BEIESP | DOI: 10.35940/ijitee.B7586.129219
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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 considered one of the chronic diseases of humankind which causes an increase in blood sugar. Many complications are reported if DM remains untreated and unidentified. Identification of this disease requires a lot of physical and mental trauma and effort which involves visiting a doctor, blood and urine test at the diagnostic center which consumes more time. Difficulties can be over crossed using the trending technology of Machine learning. The idea of the model is to prognosticate the occurrence of a diabetic with high accuracy. Therefore, two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage. Therefore two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage.
Keywords: Diabetes Mellitus, Ontology, Fine Decision Tree, SVM, Machine learning.
Scope of the Article: Machine learning