 Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseasesKalavakonda Vijaya, H. Khanna Nehemiah, A. Kannan and N.G. Bhuvaneswari International Journal of Data Mining, Modelling and Management, 2010, vol. 2, issue 4, 388-402 Abstract: In this paper, we have proposed a medical diagnosis system for predicting the severity of the cardiovascular diseases. The system is built by combining the relative advantages of fuzzy logic, neural network and genetic algorithm. The input variables that are non-discrete are fuzzified and fed as input to train the neural network. The neural network is trained using a genetic algorithm and used to identify the fuzzy rules that are significant for the purpose of classification. The rules identified by the neural network are further pruned and stored in the knowledge base. The rules in the knowledge base are used by inference and forecasting subsystem to predict the severity of the disease, for a given set of input data. Using the proposed approach, we have obtained classification accuracy of 88.35%. Keywords: fuzzy logic; neural networks; NNs; genetic algorithms; back-propagation algorithms; knowledge base; fuzzy inference; medical diagnosis; cardiovascular disease; CVD; classification; risk prediction; disease risks; disease severity; heart disease. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:2:y:2010:i:4:p:388-402 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.