Saraee, M, Koundourakis, G and Theodoulidis, B 1998, EasyMiner: data mining in medical databases , in: IEE Colloquium on Intelligent Methods in Healthcare and Medical Applications, 20 October 1998, York, UK.

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Abstract

Data mining techniques have rarely been applied to medical domain. The University of Manchester Institute of Science and Technology (UMIST) is currently in the process of experimenting with a data mining project using an extensive clinical database of stroke patients from East Lancashire to identify factors that contribute to this disease. EasyMiner is our data mining system designed and developed in the Timelab research laboratory at UMIST for interactive mining of interesting patterns in time-oriented medical databases. This system implements a wide spectrum of data mining functions, including generalisation, relevance analysis, classification and discovery of association rules. The eventual goal of this data mining effort is to identify factors that will improve the quality and cost effectiveness of patient care. In this paper, we briefly describe the EasyMiner data mining approach

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