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A Literature Review on Kidney Disease Prediction using Data Mining Classification Technique?

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.3, No. 7)

Publication Date:

Authors : ; ;

Page : 960-967

Keywords : Data Mining; Kidney Disease; Decision tree; Naive Bayes; ANN; K-NN; SVM; Rough Set; Logistic Regression; Genetic Algorithms (GAs) / Evolutionary Programming (EP); Clustering;

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The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. The Healthcare industry is generally “information rich”, which is not feasible to handle manually. These large amounts of data are very important in the field of data mining to extract useful information and generate relationships amongst the attributes. Kidney disease is a complex task which requires much experience and knowledge. Kidney disease is a silent killer in developed countries and one of the main contributors to disease burden in developing countries. In the health care industry the data mining is mainly used for predicting the diseases from the datasets. The Data mining classification techniques, namely Decision trees, ANN, Naive Bayes are analyzed on Kidney disease data set.

Last modified: 2014-08-05 02:59:09