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


Author’s Name : S Radhimeenakshi | P Nandhini Priyaunnamed

Volume 03 Issue 11 2016

ISSN no:  2348-3121

Page no: 33-40

Abstract – In recent times, coronary heart ailment is a main motive of dejection and fatality. All around the international, deaths because of coronary heart ailment is increasing hastily than from every other sickness. The prediction of the probably headaches regarding coronary heart ailment nicely earlier is a very challenging task. To become aware of the probable headaches, many structures are made that uses scientific statistics units for identifications. A number of the structures expect coronary heart disorder based totally on hazard factors. Lots of visible danger factors which can be not unusual in heart disorder patients may be used efficiently for prognosis. system primarily based on threat elements allows not best medical examiners for prediction however additionally warn the sufferers in advance about the probably presence of heart disease. Those structures also are helpful to store money and time. With this motivation, in our proposed system, we have designed a fuzzy rule based professional machine and also by way of the use of records mining approach we have decreased the whole range of attributes. The proposed system mainly specializes in cardiovascular sickness prognosis and the dataset taken from UCI. Most of the people of the researcher’s experimentation changed into made on 14 attributes out of 76. While, in this proposed work we took advantage of 6 attributes for system layout. Within the preliminary stage UCI, information participated in advised device in an effort to get results. The overall performance of the proposed work matched with Neural Network and J48 Decision Tree Algorithm.  

Keywords— Heart Disease; Fuzzy Rule Based System; Fuzzy Reasoning; Data Mining


  1. M. Mayilvaganan, K. Rajeswari, “Risk Factor Analysis to Patient Based on Fuzzy Logic Control System” , International Journal of Engineering Research and General Science Volume 2, Issue 5, August-September, 2014 ISSN 2091 -2730.
  2. Petkovic, D. (2013). Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system. Elsevier, 6.
  3. Mandeh, A., Khamforoosh, K., & Maihami, V. (2015). Data Fusion in Wireless Sensor Networks using Fuzzy Systems. International Journal of Computer Applications, 125(12).
  4. Anooj, P. (2012). Clinical decision support system: Risk level prediction. Journal of King Saud University – Computer and Information Sciences, 14.
  5. Imam, T. (2013). Association rule mining to detect factors which contribute to heart disease. Elsevier, 8.
  6. Aditya Methaila, Prince Kansal, Himanshu Arya, Pankaj Kumar, “ Early Heart Disease Detection Using Data Mining Techniques”, Computer Science and Information Technology, pp 53-59, 2014.
  7. Fabricio Voznika, Leonardo Viana, “ Data Mining Classification”.
  8. Deepali Chadna, “ Diagnosis of Heart Disease using Data Mining Algorithm”, International Journal of Computer Science and Information Technology, Vol 5(2), pp 1678-1680, 2014.
  9. Angadi S.A, Mouna M.Naravani, “ Predictiong Heart Attack using NBC, k-NN and ID3”, International Journal of Computer Science and Engineering, Vol 2 (7), pp 6-12, 2014.
  10. Jaya Rama krishniah V,V, Chandra Sekar D,V, K.Ramachand H Rao, “ Predicting the Heart Attack Symptoms by using Biomedical Data Mining Techniques”, The International Journal of computer Science and Applications, Vol 1(3), pp 10-18, May 2012.
  11. Nishara Banu, Gomathy.B, “Disease Predicting System Using Data Mining Techniques”, International Journal of Technical Research and Applications, Vol 1(5), pp 41-45, December 2013.
  12. Manikantan.V & Latha.S, “ Predicting the Analysis of Heart Disease Symptoms by using Medical Data Mining”, International Journal on Advanced Computer Theory and Engineering, Vol 2(2) pp 5-10, 2013.
  13. Jae Hong Eom, Sung-Chun Kim, Byoung-Tak Zhang, “ AptaCDSS-E:A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction”, International Journal of Expert System with Application, Vol 34(4),pp 2469-2479, May 2008.
  14. Abhishek Taneja, “ Heart Disease Prediction System using Data Mining Techniques”, Oriental Journal of Computer Science and Technology, Vol 6(4), pp 457-466, December 2013.
  15. Sunita Soni “Predictive Data Mining for Medical Diagnosis : An overview of Heart Disease Prediction”, International Journal of Computer Application, Vol 17 (8), pp 43-48, March 2011.
  16. Bala Sundar.V, “Development of Data Clustering Algorithm for predicting Heart”, International Journal of Computer Applications, vol 48 (7), pp 8-13, June 2012.
  17. Nithya N.S, “ Assessment of the risk factors of Heart Attack using frequent feature selection method”, International Journal of Communications and Engineering, Vol 1(1), pp 127-133, March 2012.
  18. Setiawan N.A, “Rule Selection for Coronary Artery Disease Diagnosis based on Rough Sets”, International Journal of Recent Trends in Engineering, Vol 12(5), pp 198-202, December 2009.
  19. Jyothi Soni, “ Intelligent and Effective Heart Disease Prediction System using Weighted Associate Classifier”, IJCSE, Vol 3(6), pp 2385-2392, June 2011.
  20. Ephzibah E.P, SundaraPandian V, “ Framing Fuzzy rules using support sets for effective Heart Disease diagnosis”, International Journal of Fuzzy Logic System, Vol 2(1), pp 11- 16, February 2012.
  21. Upasana Juneja, Deepti, “ Multiparametric Approach using Fuzzification on Heart Disease analysis”, International Journal of Advances in Computer Science and Communication Engineering, Vol 2(2), pp 1-7, June 2014.
  22. Mohd Reza Karimi Rad, Farahnaz Nouri, Masih Hashemi and Mostafa Sadeghi Moheseli, “Utilization of Fuzzy Logic for classification of Heart Disease”, International Conference on Bio-Informatics and Bio-Medical Technology, Singapore, 2012.
  23. Kantesh Kumar oad, Xu Dezhi and Pinial Khan Butt, “ A Fuzzy Rule based approach to predict risk level of Heart Disease”, Global Journal of Computer Science and Technology, Vol 14(3), 2014.