A Comprehensive Analysis of Data Mining Tools for Biomedical Data Classification

Abstract

Data mining tools are used to analyze and model data. Each of these tools has its own unique strengths and weaknesses, which make them suitable for different data mining tasks. The purpose of this chapter is to present the analysis of various data mining tools to shed light on researchers working in the field of data mining and machine learning. For this purpose, the accuracy rates of the results of different biomedical data classification applications obtained by four different data mining tools—Orange, RapidMiner, Weka, and Knime—will be evaluated. The comparisons in the context of literature research on these tools will be given. This research is particularly relevant given the increasing amount of data available in Kaggle and the need for accurate analysis and interpretation of data. By presenting the performance results of these popular data mining tools, this study will provide valuable insights for researchers and practitioners who use these tools for analysis.