The difficult and challenging problem of Medical Diagnosis using Decision Support Systems (DSS) is addressed from a system's point of view. The novel approach of Fuzzy Cognitive Maps (FCMs) as a computational modelling method has been used to tackle problem of DSS for medical problems. The FCM approach is used to address the Medical Decision Analysis and compare it with Decision Trees algorithms. Two medical problems, the bladder tumor grading and Radiotherapy are analyzed and studied using the proposed FCM approach. Simulation results been obtained previously are presented and discussed. The important contributions that have been made by using FCM in a number of medical applications are briefly discussed and future research directions are provided for the clinical use of the FCM methodology.

The mathematical model of medical problems is considered. The aim of this paper is to present a new approach in modeling the disease of Parkinson using Fuzzy Cognitive Maps (FCM). Basic theories of FCMs are reviewed and presented. Decision Support Systems (DSS) for Medical problems are considered. The disease of Parkinson is mathematically modeled using Fuzzy Cognitive Maps and three (3) experts. Linguistic variables are proposed and used to describe the correlation among concepts of the FCM. Simulations are performed and very interesting results are obtained and discussed.

Fuzzy cognitive maps constitute an important simulation methodology that combines neural networks and fuzzy logic. The Fuzzy cognitive maps designed by the experts can be enhanced significantly through learning algorithms, which proved to increase their efficiency and accuracy of simulation. Recently, learning algorithms that employ particle swarm optimization for the minimization of properly defined objective functions have been introduced. In this work, we enhance these learning schemes by incorporating local search in PSO, resulting in a memetic particle swarm optimization learning algorithm. Three variants of the memetic algorithm are applied successfully for the optimization of an Ecological Industrial Park simulation system and they are compared also with the established particle swarm optimization learning schemes. Results are reported and discussed, deriving useful conclusions

Intrapartum electronic fetal monitoring (EFM) is an indispensable means for fetal surveillance. However, the early enthusiasm was followed by scepticism, since the introduction of EFM in every day practise resulted in an increase in operative deliveries. Nevertheless the drawbacks of EFM relate not so much to the technique itself but more to the difficulties in reading and interpreting the fetal heart rate (FHR). In an attempt to develop more objective means to analyse the FHR recordings and compensate for the different levels of expertise among clinicians, computerized systems have been developed during the last 2 decades. In this work, we present an approach to automatic classification of FHR tracings belonging to hypoxic and normal newborns. The classification is performed using a set of parameters extracted from the FHR signal and two hidden Markov models (one for each class). The results are satisfactory indicating that the FHR convey much more information than what is conventionally used.

Fuzzy cognitive maps is a hybrid method based on fuzzy systems and neural networks and belonging in soft computing. The methodology of developing fuzzy cognitive maps (FCMs) is easily adaptable and relies on human expert experience and knowledge, but it exhibits weaknesses in utilization of learning methods. The external intervention (typically from experts) for the determination of FCM parameters and the convergence to undesired steady states are significant FCM deficiencies. Thus, it is necessary to overcome these deficiencies in order to improve efficiency and robustness of FCM. Weight adaptation methods can alleviate these problems by allowing the creation of less error prone FCMs where causal links-weights are adjusted through a learning process.

This research deals with the soft computing methodology of fuzzy cognitive map (FCM). Here a mathematical description of FCM is presented and a new methodology based on fuzzy logic techniques for developing the FCM is examined. The capability and usefulness of FCM in modeling complex systems and the application of FCM to modeling and describing the behavior of a heat exchanger system is presented. The applicability of FCM to model the supervisor of complex systems is discussed and the FCM-supervisor for evaluating the performance of a system is constructed; simulation results are presented and discussed.

We introduce a new algorithm for fuzzy cognitive maps learning. The proposed approach is based on the particle swarm optimization method and it is used for the detection of proper weight matrices that lead the fuzzy cognitive map to desired steady states. For this purpose a properly defined objective function that incorporates experts' knowledge is constructed and minimized. The application of the proposed methodology to an industrial control problem supports the claim that the proposed technique is efficient and robust.