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.
