Aesthetic layout of generalized trees
Software—Practice & Experience
Trading Accuracy for Simplicity in Decision Trees
Machine Learning
Communications of the ACM
Learning Problem-Oriented Decision Structures from Decision Rule: The AQDT-2 System
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Knowledge and Uncertainty: A Rough Set Approach
Proceedings of the SOFTEKS Workshop on Incompleteness and Uncertainty in Information Systems
Attribute selection and rule generation techniques for medical diagnosis systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Rough sets approach to medical diagnosis system
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
An ability of Pawlak's Rough Sets Theory to handle imprecision and uncertainty without any need of preliminary or additional information about analyzed data makes this theory very interesting for analyzing medical data. Using Rough Sets Theory knowledge extracted from raw data may be stored in form of decision rules. But increasing number and complexity of decision rules make their analysis and validation by domain experts difficult. In this paper we focus on this problem and propose an approach to visualize decision rules in form of decision trees. Afterwards domain experts validate transformed decision trees and compare the results with general guidelines proposed by the American College of Cardiology Foundation and the American Heart Association.