Open AccessAn Ontology Driven System to Predict Diabetes With Machine Learning Techniques
Author(s) -
D. R.,
Dharavath Ramesh,
B R Prakash
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b7586.129219
Subject(s) - machine learning , decision tree , artificial intelligence , diabetes mellitus , computer science , support vector machine , ontology , blood sugar , identification (biology) , decision tree learning , medicine , philosophy , botany , epistemology , biology , endocrinology
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.