Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : Vignesh Kumar K | Dr N Sumathiunnamed

Volume 04 Issue 02 2017

ISSN no:  2348-3121

Page no: 83-85

Abstract – Data mining is the process of analyzing data from perspective and summarizing it into useful information. This technique is used for finding heart diseases. Based on risk factor the heart diseases can predict easily. The main objective of this paper is to evaluate different classification techniques in heart diagnosis. First data set is to filter from effective non local means filter algorithm. After taking risk factor, data set is to classify by K-Nearest Neighbor (KNN) and Neural Network (NN).Compare to KNN, Neural Network provides better performance. After classification, performance criteria such as accuracy, precision, recall, F measure is to be calculated. The comparison measure shows that Neural Network is the best classifier for heart disease diagnosis on the existing data set. Index Terms-Heart diagnosis, Non local means filter (NLM), K-Nearest Neighbor (KNN), and Neural Network (NN)


  1. Amma, N.G.B “Cardio Vascular Disease Prediction System using Genetic Algorithm”, IEEE International Conference on Computing, Communication and Applications, 2012.
  2. Chaitrali S Dangare “Improved Study Of Heart Disease Prediction System Using Data Mining Classification Techniques”, International Journal Of Computer Applications,Vol.47,No.10 (June 2012).
  3. Beshiba Wilson, Dr.Julia Punitha Malar Dhas’ A Survey of Non-Local Means based Filters for Image Denoising‘International Journal of Engineering Research & Technology ,Vol.2 – Issue 10 (October – 2013).
  4. Y. Guo, Y. Wang, T. Hou, “ Speckle filtering of ultrasonic images using a modified nonlocal based algorithm”, Biomedical Signal Processing and control, Elsevier, pp.129-138, 2011.
  5. Pierrick Coupe, Pierre Hellier, Charles Kervram and Christian Barillot, “Bayesian non local means-based speckle filtering”,IEEE International Symposium on Biomedical Imaging,2008.
  6. Sayantan Mukhopadhyay , Shouvik Biswas , Anamitra Bardhan Roy , Nilanjan Dey’, Wavelet Based QRS Complex Detection of ECG Signal’ International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 3, May-Jun 2012, pp.2361-2365.
  7. ‘ECG Signal Analysis Using Wavelet Transforms’, C. Saritha, V. Sukanya, Y. Narasimha Murthy, Department of Physics and Electronics, S.S.B.N. COLLEGE (Autonomous), Andhra Pradesh, India
  8. Tompkins WJ, Pan J A real time QRS detection algorithm, IEEE Transactions on Biomedical Engineering BME-32, No. 3:230-235, March 1985.
  9. Algorithm M.Akhil jabbar* B.L Deekshatulua Priti Chandra International’ Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm’ Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA) 2013.
  10. MA.Jabbar,B.L Deekshatulu,Priti chandra,”Prediction of Risk Score for Heart Disease using Associative classification and Hybrid Feature Subset Selection”,In .Conf ISDA,pp 628-634,IEEE(2013)