Abstract:
Chronic Kidney Disease prediction is one of the most important issues in medical
decision making. The discovery of ckd prediction is an important task because it
depends on expertise of doctor’s knowledge. 10% of the population worldwide is
affected by chronic kidney disease (CKD), and millions die each year because they do
not have access to affordable treatment. The Global Burden of Kidney Disease 2015
study also estimated that, in 2015, 1.2 million people died from kidney failure, an
increase of 32% since 2010. Construct effective ckd prediction in time is an essential
to prevent healthy patients. Chronic kidney disease is one of the leading cause of death
and early prediction of chronic kidney disease is important. Prediction is most
interesting and challenging tasks in day to life. Data mining play an essential role for
prediction of medical dataset. With innovation and improvement in data-ware housing,
data mining, machine learning and emergence of data science as an effective field of
utilizing data as a powerful tool to predict useful information, many studies are being
conducted to make this process affective . In this study machine learning algorithms
(Logistic Regression, Decision Tree, and Random Forest Classifier) will be applied on
the health parameters associated with Chronic Kidney Disease to extract hidden
patterns on which identification will be done. Chronic Kidney Disease Prediction
System would be developed to identify Chronic Kidney Disease before time on the
basics of identified attributes and algorithm. Hence precautionary measures would be
taken in time. These precautionary measures will help to decrease the death rate caused
by Chronic Kidney Disease
