CS  Vol.7 No.6 , May 2016
Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis
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.
Cite this paper: Prema, S. and Umamaheswari, P. (2016) Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis. Circuits and Systems, 7, 701-708. doi: 10.4236/cs.2016.76059.

[1]   Koppikar, S., Baranchuk, A., Guzmán, J.C. and Morillo, C.A. (2013) Stroke and Ventricular Arrhythmias. International Journal of Cardiology, Elsevier, 7.

[2]   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.

[3]   Meyer, K.C. (2014) Diagnosis and Management of Interstitial Lung Disease. Springer Open Journal.

[4]   Prashanth, R., Roy, S.D., Mandal, P.K. and Ghosh, S. (2014) Automatic Classification and Prediction Models for Early Parkinson’s Disease Diagnosis from SPECT Imaging. Expert Systems with Applications, Elsevier, 41, 3333-3342.

[5]   Chawla, L.S., Herzog, C.A., Costanzo, M.R., Tumlin, J., Kellum, J.A., McCullough, P.A. and Ronco, C. (2014) Proposal for a Functional Classification System of Heart Failure in Patients with End-Stage Renal Disease. Journal of the American College of Cardiology, Elsevier, 63, 1246-1252.

[6]   de Dios, C., Goikolea, J.M., Colomb, F., Morenoc, C. and Vietab, E. (2014) Bipolar Disorders in the New DSM-5 and ICD-11 Classifications. Elsevier, 7, 179-185.

[7]   Ghwanmeh, S., Mohammad, A. and Al-Ibrahim, A. (2013) Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis. Journal of Intelligent Learning Systems and Applications, 5, 176-183.

[8]   Jabbar, M.A., Deekshatulu, B.L. and Chandra, P. (2013) Classification of Heart Disease Using Artificial Neural Network and Feature Subset Selection. Global Journal of Computer Science and Technology Neural & Artificial Intelligence, 13, 5-14.

[9]   Heckers, S., Barch, D.M., Bustillo, J., Gaebel, W., Gur, R., Malaspina, D., Owen, M.J., Schultz, S., Tandon, R., Tsuang, M., Van Os, J. and Carpenter, W. (2013) Structure of the Psychotic Disorders Classification in DSM 5. Schizophrenia Research, Elsevier.

[10]   Frank, A. and Asuncion, A. (2010) UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine.