A new approach for ranking fuzzy numbers by distance method
Fuzzy Sets and Systems
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Soft Computing and Fuzzy Logic
IEEE Software
Fuzzy Probabilities: New Approach and Applications (Studies in Fuzziness and Soft Computing)
Fuzzy Probabilities: New Approach and Applications (Studies in Fuzziness and Soft Computing)
Risk assessment system of natural hazards: A new approach based on fuzzy probability
Fuzzy Sets and Systems
Intelligent environment for monitoring Alzheimer patients, agent technology for health care
Decision Support Systems
Designing strategy for multi-agent system based large structural health monitoring
Expert Systems with Applications: An International Journal
CommonKADS analysis and description of a knowledge based system for the assessment of breast cancer
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Soft computing agents for e-Health in application to the research and control of unknown diseases
Information Sciences: an International Journal
Swarm intelligence and the holonic paradigm: a promising symbiosis for a medical diagnostic system
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
A theory of independent fuzzy probability for system reliability
IEEE Transactions on Fuzzy Systems
Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
IEEE Transactions on Fuzzy Systems
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In this paper, we present an agent-based system for distributed risk assessment of breast cancer development employing fuzzy and probabilistic computing. The proposed fuzzy multi agent system consists of multiple fuzzy agents that benefit from fuzzy set theory to demonstrate their soft information (linguistic information). Fuzzy risk assessment is quantified by two linguistic variables of high and low. Through fuzzy computations, the multi agent system computes the fuzzy probabilities of breast cancer development based on various risk factors. By such ranking of high risk and low risk fuzzy probabilities, the multi agent system (MAS) decides whether the risk of breast cancer development is high or low. This information is then fed into an insurance premium adjuster in order to provide preventive decision making as well as to make appropriate adjustment of insurance premium and risk. This final step of insurance analysis also provides a numeric measure to demonstrate the utility of the approach. Furthermore, actual data are gathered from two hospitals in Mashhad during 1year. The results are then compared with a fuzzy distributed approach.