Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy logic and probability applications: bridging the gap
Fuzzy logic and probability applications: bridging the gap
Using formal concept analysis to design and improve multidisciplinary clinical processes
WSEAS Transactions on Information Science and Applications
Comparative genome sequence analysis by efficient pattern matching technique
WSEAS Transactions on Information Science and Applications
Hierarchies generated for data represented by fuzzy ternary relations
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
Lattices for 3-dimensional fuzzy data generated by fuzzy Galois connections
WSEAS Transactions on Systems and Control
Conceptual fuzzy temporal relational model (FTRM) for patient data
WSEAS Transactions on Information Science and Applications
Neural Network Approaches to Grade Adult Depression
Journal of Medical Systems
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The purpose of this study is to apply fuzzy theory on health care. To achieve this goal, Beck Depression Inventory (BDI)-II was adopted as the instrument and outpatients of a psychiatric clinic were recruited as samples and undergraduates as non-clinical sample as well. To elicit the membership degree, we asked the subjects are free to choose more than one alternative for each item listed in BDI and, in turn, assign percentages on the chosen alternatives. Moreover, the sum of percentages of the chosen categories is restricted to 100%. We performed the possibility- based (fuzzy c-means, FCM) and probability-based classification (Wald's method and k-means) to classification of severity of depression. The scoring of BDI of subjects were analyzed by clustering analysis while the diagnose of depression-severity by a psychiatrist was used as the criterion to evaluate classification accuracy. The percentage of correct classification among FCM, Wald's method and k-means were compared. The analytical results show the Kendall's τ coefficient of FCM, Wald's method and k-means were .549, .316, and .395, respectively. That is, FCM exhibited a higher association between the original and classified membership than did Wald's and k-means methods. We concluded that FCM identified the data structure more accurately than the two crisp clustering methods. It is also suggested that considerable cost concerning prevention and cure of depression might be reduced via FCM.