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[Knowledge discovery in database and its application in clinical diagnosis]
1Department of Electronic Engineering, Dalian University of Technology, Dalian 116024, China.
Summary
This study introduces knowledge discovery in databases and data mining techniques for analyzing large medical datasets. It highlights rough set theory as a valuable tool for uncovering crucial data patterns beyond traditional statistical methods.
Area of Science:
- Computer Science
- Data Science
- Artificial Intelligence
Context:
- Medical databases contain vast amounts of data exceeding human comprehension.
- Traditional statistical methods are insufficient for analyzing large-scale medical data.
- The need for advanced analytical techniques in healthcare is growing.
Purpose:
- To introduce the concepts of knowledge discovery in databases (KDD) and data mining.
- To describe the principles and applications of rough set theory.
- To demonstrate the utility of rough set theory in analyzing complex datasets.
Summary:
- Knowledge discovery in databases and data mining offer powerful methods for extracting insights from large datasets.
- Rough set theory provides a mathematical framework for dealing with uncertainty and vagueness in data.
- The paper presents examples illustrating the application of rough set theory in pattern discovery.
Impact:
- Facilitates more effective analysis of complex medical data.
- Enables the identification of hidden patterns and knowledge within large databases.
- Supports improved decision-making through data-driven insights.