Comparative Study On Classification Methods To Diagnosis The Diabetics

Document Type : Primary Research paper


Bannari Amman Institute of Technology, Erode, India


The information disclosure from clinical datasets is amazingly huge to make a
successful clinical conclusion. The objective of the Machine Learning is to gain information
from a dataset and adjust it into a fitting structure for additional utilization. Diabetic
Mellitus remains as a broadly rising incessant infection, and this is an incredible test
around the world. Today, it is basic in different age bunches extent as of youngsters to
grown-ups. As the quantity of Diabetic Mellitus persistent have been multiplying each year
explicitly in India. In the proposed work, comparative study on various classification algorithm
such as Naïve Bayes, Random Forest, Decision tree, K Nearest Neighbor (KNN) ,
Support Vector Machine(SVM) on dataset to predict whether the given person affected
with diabetic or not. In this work, a new ensemble method is identified to provide better
accuracy such as 85.44 % compared with existing classification algorithm.