Knowledge discovery in clinical databases based on variable precision rough set model

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 informatics. 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 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, this system is applied to a clinical database of meningoencephalitis, whose computational results show that several new findings are obtained.