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Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases

Author

Listed:
  • Kalavakonda Vijaya
  • H. Khanna Nehemiah
  • A. Kannan
  • N.G. Bhuvaneswari

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%.

Suggested Citation

  • Kalavakonda Vijaya & H. Khanna Nehemiah & A. Kannan & N.G. Bhuvaneswari, 2010. "Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 2(4), pages 388-402.
    • Handle: RePEc:ids:ijdmmm:v:2:y:2010:i:4:p:388-402
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    Cited by:

    1. Victor E. Ekong & Udoinyang G. Inyang & Emmanuel A. Onibere, 2012. "Intelligent Decision Support System for Depression Diagnosis Based on Neuro-fuzzy-CBR Hybrid," Modern Applied Science, Canadian Center of Science and Education, vol. 6(7), pages 1-79, July.

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