Rough classification of patients after highly selective vagotomy for duodenal ulcer
International Journal of Man-Machine Studies
Fuzzy sets in pattern recognition: methodology and methods
Pattern Recognition
Genetic algorithms for learning in fuzzy relational structures
Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Sequential Pattern Recognition: Naive Bayes Versus Fuzzy Relation Method
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Computer-aided sequential diagnosis using fuzzy relations – comparative analysis of methods
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Sequential classification via fuzzy relations
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Leukemia prediction from gene expression data—a rough set approach
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Rough sets and fuzzy sets theory applied to the sequential medical diagnosis
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
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Sequential classification task is typical in medical diagnosis, when the investigations of the patient's state are repeated several times. Such situation takes place in controlling of the drug therapy efficacy. In this paper the methods of sequential classification using rough sets theory are developed and evaluated. The proposed algorithms, using the set of learning sequences, calculate the lower and upper approximations of the set of proper decision formulas and then use them to make final decision. Depending on the input data different algorithms are derived. Next, all presented algorithms were practically applied in computer-aided recognition of the human acid-base state balance and the results of comparative experimental analysis of in respect of classification accuracy are also presented and discussed.