Weka vs Rapid Miner: A Performance Analysis of Data Mining Classification Techniques on Health Data

Abstract

The term "data mining" is helpful since it simplifies the process of looking through and analyzing a large amount of data to find information that is important and confidential. Academics have recently shown a rising interest in the management of healthcare statistics using data mining techniques. The authors of this study used data mining techniques to attempt to categorize three distinct datasets related to breast cancer, diabetes, and renal disease using weka and fast miner. A variety of classification methods, including Decision Tree, K-Nearest Neighbors, Naive Bayes, Random Forest, and Support Vector, are used in the performance evaluation. Every categorization technique is implemented utilizing well-known data mining and tools for knowledge discovery like Rapid miner and weka. Weka tools outperform Rapid Miner tools in terms of accuracy across all datasets. Data mining is an appropriate word that makes it easier to explore and analyze huge amounts of data in search of private and useful information. Data mining approaches have recently piqued the interest of researchers who want to handle healthcare statistics. In this study, weka and Rapid miner were used to attempt classification using data mining techniques on three datasets (breast cancer, diabetes, and kidney). The performance of various classification techniques, including Decision Tree, K-Nearest Neighbors, Naive Bayes, Random Forest, and Support Vectors, is compared. Weka and Rapid miner, two popular data mining and knowledge discovery tools, are used for every categorization approach. When compared to Rapid Miner tools, Weka tools have been demonstrated to work with superior accuracy overall.