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

Author - Guneet Kaur, Ajay Sharma

Published : Volume-5,Issue-4  ( Apr, 2018 )

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