Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7043
Title: Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction
Authors: Lavrač, Nada
Kononenko, Igor
Keravnou-Papailiou, Elpida
Keywords: Computer science
Artificial intelligence
Data reduction
Diagnosis
Databases
Issue Date: 1998
Publisher: IOS Press
Source: AI communications, 1998, Volume 11, Issue 3-4, Pages 191-218
Abstract: Extensive amounts of knowledge and data stored in medical databases request the development of specialized tools for storing and accessing of data, data analysis, and effective use of stored knowledge and data. This paper focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension. The paper sketches the history of research that led to the development of current intelligent data analysis techniques, discusses the need for intelligent data analysis in medicine, and proposes a classification of intelligent data analysis methods. The main scope of the paper are machine learning and temporal abstraction methods and their application in medical diagnosis. A selection of methods and diagnostic domains is presented, and the performance and usefulness of approaches discussed. The paper concludes with the evaluation of selected intelligent data analysis methods and their applicability in medical diagnosis
URI: http://ktisis.cut.ac.cy/handle/10488/7043
ISSN: 0921-7126 (print)
1875-8452 (online)
DOI: https://iospress.metapress.com/content/eue0a6blqjt9q0t0/resource-secured/?target=fulltext.pdf
Rights: Copyright IOS Press All rights reserved.
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