Paper Title
Predict Chronic Kidney Disease using Data Mining Algorithms in HADOOP
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 ( K- nearest neighbor ) and SVM ( Support Vector Machine)
data mining algorithms are used. From the given dataset, six statistical parameters are generated which are as follows:
1. Accuracy %
2. Error %
3. Precision
4. Recall
5. F1 error
6. 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.
Keywords- Big Data , Chronic Kidney Disease , Data Mining , Hadoop , KNN , MATLAB , SVM
Nomenclature:
ANN Artificial Neural Network
CKD Chronic Kidney Disease
KNN K- Nearest Neighbor
MATLAB Matrix Laboratory
SVM Support Vector Machine