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Kasabov, N., Feigin, V., Hou, Z.-G., Chen, Y.X., Liang, L., Krishnamurthi, R., Othman, M. and Parmar, P. (2014) Evolving Spiking Neural Networks for Personalised Modelling, Classification and Prediction of Spatio-Temporal Patterns with a Case Study on Stroke. Neuro Computing, Elsevier, Vol. 134, 269-279.

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• TITLE: Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

  AUTHORS: S. Prema, P. Umamaheswari

  KEYWORDS: Classification Model, Disease Diagnosis, Rough Set Model, Feature Selection, Multitude Classifier, Mercer Naïve

  JOURNAL NAME: Circuits and Systems, Vol.7 No.6, May 12, 2016

  ABSTRACT: Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, bio informatics, crime prediction and so on. However, an efficient disease diagnosis model was compromised the disease prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC) is developed to improve the disease prediction rate and enhance the class accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially, a Rough Set model is used for Feature Selection aiming at minimizing the execution time for obtaining the disease feature set. A Multitude Classifier model is presented in second step for detection of heart disease and for efficient classification. The Na?ve Bayes Classifier algorithm is designed for efficient identification of classes to measure the relationship between disease features and improving disease prediction rate. Experimental analysis shows that RS-RMC is used to reduce the execution time for extracting the disease feature with minimum false positive rate compared to the state-of-the-art works.