Expert systems in psychiatry: A review
Journal of Medical Systems
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Self-organizing maps
Entropy-based fuzzy clustering and fuzzy modeling
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Empirical sensitivity analysis for computational procedures
Proceedings of the 2005 conference on Diversity in computing
Some studies on mapping methods
International Journal of Business Intelligence and Data Mining
Some studies on fuzzy clustering of psychosis data
International Journal of Business Intelligence and Data Mining
An investigation of neuro-fuzzy systems in psychosomatic disorders
Expert Systems with Applications: An International Journal
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
International Journal of Computational Intelligence Studies
A case-based knowledge system for safety evaluation decision making of thermal power plants
Knowledge-Based Systems
Neural Network Approaches to Grade Adult Depression
Journal of Medical Systems
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One of the key objectives behind implementation of computer logic in medicine is to mimic doctors' medical logic. The present work is a novel attempt to develop fuzzy logic-based expert systems (ESs), which are able to reason like doctors for screening adult psychosis. Among several techniques of fuzzy classifier (FC)-design, clustering-to-classifier technique (CCT) has been adopted, in this paper. We have clustered a set of statistically generated psychosis data (with 24 factors and 7 responses) using (i) fuzzy C-means (FCM) algorithm, (ii) entropy-based fuzzy clustering (EFC) algorithm and its proposed extensions. The properties of the best set of clustered data are then utilized to develop the respective FCs. The number of rules of the FC is made equal to the number of clusters obtained above and the attributes of the cluster centers carry information of the rule base of the said FC. Moreover, a genetic algorithm (GA) has been used to tune the data base of the FC for further improvement of its performance. The performances of these FCs are tested on a set of randomly-generated test psychosis cases and another set of diagnosed cases. It is found that for both the data sets, each of the FCs is appreciably accurate in inferring and the classifier developed based on FCM-clustered data slightly outperforms the FC developed from EFC-clustered data. It may happen due to the fact that the performance of the developed FC depends on the nature of clusters also.