Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
A two-objective fuzzy k-cardinality assignment problem
Journal of Computational and Applied Mathematics
Brain tumor characterization using the soft computing technique of fuzzy cognitive maps
Applied Soft Computing
Computer Methods and Programs in Biomedicine
Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
An interpretable fuzzy rule-based classification methodology for medical diagnosis
Artificial Intelligence in Medicine
Advanced soft computing diagnosis method for tumour grading
Artificial Intelligence in Medicine
Differential Evolution for learning the classification method PROAFTN
Knowledge-Based Systems
A group decision making procedure for fuzzy interactive linear assignment programming
Expert Systems with Applications: An International Journal
A learning method for developing PROAFTN classifiers and a comparative study with decision trees
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Advanced sensitivity analysis of the fuzzy assignment problem
Applied Soft Computing
Integration of expert knowledge and image analysis techniques for medical diagnosis
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Automatic parameter settings for the PROAFTN classifier using hybrid particle swarm optimization
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Computers in Biology and Medicine
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The aim of this paper is to provide a concise portrayal of medical applications of a new fuzzy classification method called PROAFTN, which uses a multicriteria decision aid approach. The review summarises and discusses medical applications of the proposed method in acute leukemia, astrocytic and bladder tumours. Although still an investigative method, the preliminary results are very encouraging and demonstrate the potential performances of this procedure for solving medical classification problems.