Medical data: their acquisition, storage, and use
Medical informatics: computer applications in health care
Medical decision making: probabilistic medical reasoning
Medical informatics: computer applications in health care
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Discovery of Rules about Compilations - A Rough Set Approach in Medical Knowledge Discovery
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
A Closest Fit Approach to Missing Attribute VAlues in Preterm Birth Data
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Rough sets to help medical diagnosis - Evidence from a Taiwan's clinic
Expert Systems with Applications: An International Journal
Rough Sets for Handling Imbalanced Data: Combining Filtering and Rule-based Classifiers
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
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We discuss a process of analysing medical diagnostic data by means of the combined rule induction and rough set approach. The first step of this analysis includes the use of various techniques for discretization of numerical attributes. Rough sets theory is applied to determine attribute importance for the patients' classification. The novel contribution concerns considering two different algorithms inducing either minimum or satisfactory set of decision rules. Verification of classification abilities of these rule sets is extended by an examination of sensitivity and specificity measures. Moreover, a comparative study of these composed approaches against other learning systems is discussed. The approach is illustrated on a medical problem concerning anterior cruciate ligament (ACL) rupture in a knee. The patients are described by attributes coming from anamnesis, MR examinations and verified by arthroscopy. The clinical impact of our research is indicating two attributes (PCL index, age) and their specific values that could support a physician in resigning from performing arthroscopy for some patients.