Title: Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases

Authors: Kalavakonda Vijaya, H. Khanna Nehemiah, A. Kannan, N.G. Bhuvaneswari

Addresses: Department of Computer Science and Engineering, College of Engineering Guindy, Anna University Chennai, Chennai, Tamilnadu, 600025, India. ' Ramanujan Computing Centre, College of Engineering Guindy, Anna University Chennai, Chennai, Tamilnadu, 600025, India. ' Department of Computer Science and Engineering, College of Engineering Guindy, Anna University Chennai, Chennai, Tamilnadu, 600025, India. ' Department of Computer Science and Engineering, Noorul Islam University, Kumaracoil, Thuckalay, Kanyakumari District, Tamilnadu, 629180, India

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.

DOI: 10.1504/IJDMMM.2010.035565

International Journal of Data Mining, Modelling and Management, 2010 Vol.2 No.4, pp.388 - 402

Published online: 30 Sep 2010 *

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