Effect of WEKA Filters on the Performance of the NavieBayes Data Mining Algorithm on Arrhythmia and Parkinson’s Datasets - Shaikh, Tawseef Ayoub; Chhabra, Amit (International Journal of Computer Sciences and Engineering, 2014)
Data mining is the process of selecting, exploring and modeling a large database in order to discover model and pattern that are unknown [1]. Enormous gathered data in Health care Information society are scattered with different archive systems which are not connected with one another. This unorganized data leads to delay in monitoring, improper planning, defocus the analysis which leads to inaccuracy in decision making. The purpose of this study is to explore Supervised and Non Supervised WEKA filters on the data mining algorithm NavieBayes which is used for classification the data sets of Arrhythmia and Parkinson’s diseases. This in turn helps in increasing the performance accuracy of the classifier used for knowledge discovery [2] . Both the Datasets were taken from UCI Repository [3].