DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-5,Issue-6 ( Jun, 2017 ) | |||||||||
Paper Title |
Predict Chronic Kidney Disease using Data Mining Algorithms in Hadoop | |||||||||
Author Name |
Guneet Kaur, Ajay Sharma | |||||||||
Affilition |
M.Tech Student, Department of Computer Science and Engineering, A.P. of CSE dept., Amritsar College of Engineering and Technology, Manawala, Amritsar | |||||||||
Pages |
45-49 | |||||||||
Abstract |
Today, Chronic diseases are booming day by day in our country due to the style of living of an individual and due to their hereditary issue. Above all, Chronic Kidney Disease is currently considered as the most common problem these days. Data mining is the area of analysis used to extract the hidden figures for decision making. Big data can be considered as the environment to store or handle the large volumes of structured, unstructured and semi-structured data. In the era of big data, the data mining techniques are applied to examined the Chronic Kidney Diseases. The prime objective of this paper is to analyze the data from a Chronic Kidney Disease dataset using classification techniques such as K-Nearest Neighbor (KNN), Naive Bayes , Support Vector Machine(SVM) and dimensionality reduction algorithms such as Principle Component Analysis(PCA) and Independent Component Analysis(ICA) in MATLAB by accessing Hadoop in itself in order to examine the disease in an individual or to raise the level of accuracy and to detect the elapsed time . Furthermore, supervised learning is utilized for pre-analysis and processing while classifying the disease in an individual. Keywords - Data mining, Chronic Kidney Disease, Hadoop, Big data, K-NN, Naive Bayes, PCA, ICA Nomenclature: CKD Chronic Kidney Disease K-NN K-Nearest Neighbor PCA Principle Component Analysis ICA Independent Component Analysis SVM Support Vector Machine MATLAB Matrix Laboratory | |||||||||
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