| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1997: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1996: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Research Abstract |
Since a large amount of clinical data are being stored electronically, discovery of knowledge from such clinical databases is one of the important growing research area in medical Informatica. For this purpose, we develop KDD-R (a system for knowledge Discovery in Databases using Rough sets), an experimental system for knowledge discovery and machine learning research using variable precision rough sets (VPRS) model, which is an extension of original rough set model. This system works in the following steps. First, it preprocesses databases and translates continuous data in to discretized ones. Second, KDD-R checks dependencies between attributes and reduces spurious data. Third, the system computes rules from reduced databases. Finally, fourth, it evaluates decision making. For evaluation, this system is applied to a clinical database of meninigenecephalitis, whose computational results show that everal new findings are obtained.
Knowledge discovery in clinical databases is an important research area in medical informatics. Most of medical data, such as patient records, laboratory data, are now being stored electronically, and the amount of clinical databases will be too huge, so that even medical experts cannnot deal with such large databases. Thus, a computer-based approach is promising to solve this difficult situation. In this study, we introduce a system KDD-R (a system for Knowledge Discovery in Databases using Rough sets), based on Variable Precision Rough Set (VPRS) model.
This system works as follows. First, it preprocesses databases and translates continuous data into discretized ones. Second, KDD-R checks dependencies between attributes and reduces spurious data. Third, the system computes rules from reduced databases. Finally, fourth, it evaluates decision making.
For evaluation, we apply KDD-R to a clinical database of meningoencephalitis, whose computational results show that several new findings are obtained from clinical databases.
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