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What is Biomedical Data

Methodologies, Frameworks, and Applications of Machine Learning
Any data regarding health status that can be processed for the purpose of obtaining information.
Published in Chapter:
A Comprehensive Analysis of Data Mining Tools for Biomedical Data Classification: Assessing Strengths, Weaknesses, and Future Directions
Sibel Senan (Istanbul University-Cerrahpasa, Turkey) and Seda Keskin Tasci (Istanbul University-Cerrahpasa, Turkey)
Copyright: © 2024 |Pages: 16
DOI: 10.4018/979-8-3693-1062-5.ch012
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.
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More Results
Security Risks of Biomedical Data Processing in Cloud Computing Environment
Row facts related to the health status that can be processed to get information.
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