Description of Invention
There are millions of sample medical cases recorded in many digital medical datasets that can be used by the data mining techniques for predicting any particular disease. Improving the classification accuracy in medical diagnosis based on patterns extracted from the available medical datasets is a challenging research problem as the medical datasets contain many complex patterns. In artificial intelligence, hybrid intelligent systems and multi-agent systems can support the data mining process to improve the accuracy of classification for medical diagnosis. Hybrid intelligent system is an integrated design of different artificial intelligence techniques such as neuro-fuzzy, genetic-fuzzy, etc., that has been successful in many applications such as data mining, computer vision, speech synthesis, etc. Multi-agent system is a software system built with several autonomous agents where the agents communicate with each other and find solutions for the complicated problems. This paper proposes a multi-agent system design based on the hybrid fuzzy approach for the classification of medical data. Wisconsin diagnostic breast cancer (WDBC) dataset is used for testing the proposed system. The proposed system showed an accuracy of 96% for the classification of breast cancer disease.