Diabetes is a chronic condition or a series of metabolic disorders where a person hurts from a higher blood glucose level in the body. The insulin making is insufficient because the body's cells do not respond suitably to insulin. Constant diabetes hyperglycemia is associated with long-term damage, brokenness and failure of multiple organs, particularly the kidneys, ewyes, heart, veins, and nerves. This study aims to present a comparative analysis among three popular machine taxonomy processes namely Support Vector Machine, Naive Bayes, and Decision Tree, used to perceive diabetes at a primary stage in a patient. In this work, we have tried to brief the most important machine learning algorithm with full accuracy to predict diabetes disease in a patient.
Key words: Diabetes, Decision Tree, Support Vector Machine, Naive Byes, Decision Tree; Accuracy; Machine Learning, Central tendency, Dispersion.
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