The inclusion of Information Technology in various fields like that of health care has proven to boost the existing progressions at all times, like Machine Learning algorithms that automate the manual effort of people. The advancements in the health care sector are always in demand and are studied upon. With the growing population and reckless lifestyles, the urgency to find proper tools and precautions for substantial diseases is increasing. The prediction algorithms have been providing the sufferer with a head start to adequate treatment and diagnosis. A significant part of the human population suffers from diabetes. The disease can affect the immune system costly by inducing various other diseases. Diabetes is the disease that is focused on in this paper, by using two Machine Learning Classification Algorithms to conclude with the better performing classifier while predicting if a person might have diabetes by a given set of data along with the deliberations on the main characteristics and significance of the two classifiers namely Gradient Boosting (GrB) classfier and Extra Trees (ExT) Classifiers. And the latter section will explain why GrB Classifier surpasses ExT Classifier when it comes to predicting diabetes in a patient. As Accuracy percentage of GrB is 73.3precentage.

Nirjharini Mohanty, Soumen Nayak, Monarch Saha, Vishal Baral, Imlee Rout
Siksha O Anusandhan (Deemed to be University), India

Machine Learning, GrB, ExT, Classifier, Diabetes
Published By :
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 2 , Issue: 2 )
Date of Publication :
March 2021

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.