Fuzzy set theory in medical diagnosis
IEEE Transactions on Systems, Man and Cybernetics
Formal ontology, conceptual analysis and knowledge representation
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
A unified parameterized formulation of reasoning in fuzzy modeling and control
Fuzzy Sets and Systems
Expert Systems: Design and Development
Expert Systems: Design and Development
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Neuro-fuzzy classification of prostate cancer using NEFCLASS-J
Computers in Biology and Medicine
Integration of prostate cancer clinical data using an ontology
Journal of Biomedical Informatics
Prognosis of prostate cancer by artificial neural networks
Expert Systems with Applications: An International Journal
A novel case based reasoning approach to radiotherapy planning
Expert Systems with Applications: An International Journal
An ontology-based fuzzy decision support system for multiple sclerosis
Engineering Applications of Artificial Intelligence
Efficient inhomogeneity compensation using fuzzy c-means clustering models
Computer Methods and Programs in Biomedicine
Generalized rough fuzzy c-means algorithm for brain MR image segmentation
Computer Methods and Programs in Biomedicine
Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering
Computer Methods and Programs in Biomedicine
A heart disease recognition embedded system with fuzzy cluster algorithm
Computer Methods and Programs in Biomedicine
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
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This paper deals with application of fuzzy intelligent systems in diagnosing severity level and recommending appropriate therapies for patients having Benign Prostatic Hyperplasia. Such an intelligent system can have remarkable impacts on correct diagnosis of the disease and reducing risk of mortality. This system captures various factors from the patients using two modules. The first module determines severity level of the Benign Prostatic Hyperplasia and the second module, which is a decision making unit, obtains output of the first module accompanied by some external knowledge and makes an appropriate treatment decision based on its ontology model and a fuzzy type-1 system. In order to validate efficiency and accuracy of the developed system, a case study is conducted by 44 participants. Then the results are compared with the recommendations of a panel of experts on the experimental data. Then precision and accuracy of the results were investigated based on a statistical analysis.