Abstract:
Diabetes is a killer. It causes heart attack, visual deficiency, kidney infections, and more. Patients visit an indicative center, consult their specialist, and wait a day or more for their results. Every time they need their conclusion report, they have to waste money.
However, with the rise of Machine Learning methods, we can solve this problem by using data mining to predict whether a patient has diabetes.
Early diagnosis allows patients to be treated before it becomes critical. Information mining can uncover diabetes-related hidden learning.
Thus, it’s more important than ever in diabetes testing. This research aims to create a framework that can better predict a patient’s diabetic risk. This study built a framework using Decision Tree, Naïve Bayes, and Support Vector Machine calculations.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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