PREDICT CHRONIC KIDNEY DISEASE USING DATA MINING ALGORITHMS IN HADOOP

Authors

  • Guneet Kaur M.Tech Student, Department of Computer Science and Engineering, Amritsar College of Engineering and Technology, Amritsar Author
  • Er. Ajay Sharma 2A.P. of CSE dept. Amritsar College of Engineering and Technology, Amritsar Author

Keywords:

Big Data , Chronic Kidney Disease , Data Mining , Hadoop , KNN , MATLAB , SVM

Abstract

This paper introduced the chronic kidney disease prediction with data mining algorithms. In the 21st century,
chronic kidney diseases are growing so rapidly and it plays a key role in an individual's life. To obtain the
hidden information from the given dataset, data mining is used to make the decisions. Big data is the latest
technology used to store and process the voluminous data and that data can be structured data, unstructured
data and semi-structured data . In this paper, to predict or detect the chronic kidney disease , KNN ( Knearest neighbor ) and SVM ( Support Vector Machine) data mining algorithms are used. From the given
dataset, six statistical parameters are generated which are as follows:
i. Accuracy %
ii. Error %
iii. Precision
iv. Recall
v. F1 error
vi. Elapsed Time
The whole research has done in the layered form in order to enhance the above statistical parameters that have
mentioned to predict the chronic kidney disease. MATLAB is a tool used to perform to prediction of chronic
kidney disease by accessing Hadoop in itself.

Downloads

Published

2018-02-28

Issue

Vol. 5 No. 2 (2018): February

Section

Articles