Authors :
Dr. B. Premamayudu; K. Muralikrishna; K. Pramodh
Volume/Issue :
Volume 7 - 2022, Issue 5 - May
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3NPlZq4
DOI :
https://doi.org/10.5281/zenodo.6642320
Abstract :
Diabetes is a chronic disease caused due to high
amount of glucose present in the human body. If this
diabetes is ignored, this may lead to severe health
problems such as kidney failure, heart attacks, blood
pressure, eye damage, weight loss, frequent urination, etc.
Basically, human body contains Insulin which is produced
by pancreas. This insulin helps to enter glucose in to blood
cells in order to generate energy to the body. There are
types in diabetes Type1 and Type 2 other form is
gestational diabetes which is caused during pregnancy.
This can be controlled in the earlier stages of the attack.
According to International Diabetes Federation (IDF) 382
million people are suffering with diabetes and by next
20years the count will be doubled as 592 million. To
accomplish this goal, in this project we can do early
prediction of diabetes in humans or patients for good
accuracy through applying various machine learning
techniques such as Random Forest (RF), K-nearest
neighbors (KNN), Decision Trees (DT), etc. However, in
this project we are predicting diabetes using KNN
classifier model. As we see now a days machine learning
is an emerging technology and boon to many problem
solutions.
Diabetes is a chronic disease caused due to high
amount of glucose present in the human body. If this
diabetes is ignored, this may lead to severe health
problems such as kidney failure, heart attacks, blood
pressure, eye damage, weight loss, frequent urination, etc.
Basically, human body contains Insulin which is produced
by pancreas. This insulin helps to enter glucose in to blood
cells in order to generate energy to the body. There are
types in diabetes Type1 and Type 2 other form is
gestational diabetes which is caused during pregnancy.
This can be controlled in the earlier stages of the attack.
According to International Diabetes Federation (IDF) 382
million people are suffering with diabetes and by next
20years the count will be doubled as 592 million. To
accomplish this goal, in this project we can do early
prediction of diabetes in humans or patients for good
accuracy through applying various machine learning
techniques such as Random Forest (RF), K-nearest
neighbors (KNN), Decision Trees (DT), etc. However, in
this project we are predicting diabetes using KNN
classifier model. As we see now a days machine learning
is an emerging technology and boon to many problem
solutions.
