A Novel Fuzzy Expert System Design for Predicting Heart Diseases

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Arash Khormehr, Vafa Maihami

Arash Khormehr and Vafa Maihami. Article: A Novel Fuzzy Expert System Design for Predicting Heart Diseases. International Journal of Computer Applications 138(4):33-38, March 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Arash Khormehr and Vafa Maihami},
	title = {Article: A Novel Fuzzy Expert System Design for Predicting Heart Diseases},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {138},
	number = {4},
	pages = {33-38},
	month = {March},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The volume of data generated by rapid technological progress also on the rise is too fast, use, the selection of useful data and its analysis of the issues that have been of interest to researchers, obtain conclusive results due to the uncertainty of this information to resolve these issues also are research priorities. Forecast diseases, including the risk factors in the selection of important and complex issues that concern is to get the correct result is that Heart disease is no exception. In this paper, using a fuzzy system a model is designed to predict heart disease that using design rules based on medical science works. By a physician, a series of rules designed, with this rules and fuzzy systems a good model with more efficient for predicting heart disease is presented. The proposed algorithm is based on data obtained from several cardiac patients and healthy individuals were tested in Tohid Hospital in Sanandaj city, the proposed algorithm's accuracy from 95% in people prone to heart disease to be identified with precision.


  1. Anooj, P. (2012). Clinical decision support system: Risk level prediction. Journal of King Saud University – Computer and Information Sciences, 14.
  2. Austin, P. C. (2013). Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. Elsevier, 10.
  3. Chitra, R. (2013). Heart Disease Prediction System Using Supervised Learning Classifier. Bonfring International Journal of Software Engineering and Soft Computing, 7.
  4. Czabanski, R. (2013). Fetal state assessment using fuzzy analysis of fetal heart rate signals—Agreement with the neonatal outcome. Elsevier.
  5. Dai, W. (2014). Prediction of hospitalization due to heart diseases by supervised learning methods. Elsevier, 9.
  6. Das, R. (2010). Effective diagnosis of heart disease through neural networks ensembles. Elsevier, 6.
  7. Imam, T. (2013). Association rule mining to detect factors which contribute to heart disease. Elsevier, 8.
  8. jabbar, M. (2013). Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm. elsevier, 10.
  9. Jee, S. H. (2015). A coronary heart disease prediction model: the Korean Heart Study. dx.doi.org, 10.
  10. K.Rajeswari. (2012). Feature Selection in Ischemic Heart Disease Identification using Feed Forward Neural Networks. elsevier, 6.
  11. Khatibi, V. (2010). A fuzzy-evidential hybrid inference engine for coronary heart disease. elsevier, 7.
  12. Kolus, A. (2015). Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate. Elsevier, 11.
  13. Krishnaiah, V. (2013). Diagnosis of Heart Disease Patients Using Fuzzy Classification Technique.
  14. Kumar, Y. (2014). Research Aspects of Expert System. International Journal of Computing & Business Research, 11.
  15. Laura Sabiani, M. (2015). Intra- and interobserver agreement among obstetric experts in court regarding the review of abnormal fetal heart rate tracings and obstetrical management. Am J Obstet Gynecol 2015;213:856.e1-8., p. 8.
  16. M, I. (2015). Efficient Data Mining Method to Predict the Risk of Heart Diseases. 4th International Conference on Eco-friendly Computing and Communication Systems, ICECCS 2015 (p. 7). Procedia.
  17. Muthukaruppan, S. (2012). A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. elsevier, 9.
  18. Nahar, J. (2013). Computational intelligence for heart disease diagnosis: A medical knowledge driven approach. elsevier, 9.
  19. Palaniappan, S. (2008). Intelligent Heart Disease Prediction System Using Data Mining Techniques. IJCSNS International Journal of Computer Science and Network Security, 8.
  20. Petkovic, D. (2013). Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system. Elsevier, 6.
  21. Setoa, E. (2012). Developing healthcare rule-based expert systems: Case study of a heart failure telemonitoring system. ijmijournal, 10.
  22. Sharma, A. (2014). Emerging Applications of Data Mining for Healthcare Management - A Critical Review Management - A Critical Review. 6.
  23. Tay, D. (2015). A novel neural-inspired learning algorithm with application to clinical risk prediction. Journal of Biomedical Informatics, 10.
  24. Fathi, M., Maihami, V., & Moradi, P. (2013). Reinforcement Learning for Multiple Access Control in Wireless Sensor Networks: Review, Model, and Open Issues. Wireless personal communications, 72(1), 535-547.
  25. Mandeh, A., Khamforoosh, K., & Maihami, V. (2015). Data Fusion in Wireless Sensor Networks using Fuzzy Systems. International Journal of Computer Applications, 125(12).


Predict heart disease, Fuzzy systems, Fuzzy inference engine, Risk factor.