A new version of the rule induction system LERS
Fundamenta Informaticae
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Knowledge and Uncertainty: A Rough Set Approach
Proceedings of the SOFTEKS Workshop on Incompleteness and Uncertainty in Information Systems
Applying rough set theory to multi stage medical diagnosing
Fundamenta Informaticae
Rough sets to help medical diagnosis - Evidence from a Taiwan's clinic
Expert Systems with Applications: An International Journal
Visualization of Rough Set Decision Rules for Medical Diagnosis Systems
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Data preparation for data mining in medical data sets
Transactions on rough sets VI
Selection of important attributes for medical diagnosis systems
Transactions on rough sets VII
Complex Decision Systems and Conflicts Analysis Problem
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Pawlak's Rough Sets Theory is one of many mathematical approaches to handle imprecision and uncertainty. The main advantage of the theory over other techniques is that it does not need any preliminary or additional information about analyzed data. This feature of rough set theory favors its usage in decision systems where new relations among data must be uncovered. In this paper we use data from a medical data set containing information about heart diseases and applied drugs to construct a decision system, test its classification accuracy and propose a methodology to improve an accurateness and a testability of generated “if-then” decision rules.