Heart Disease Diagnosis Using C4.5 Algorithms - A Case Study

Ali Idri, Ilham Kadi, Halima Benjelloun


Data mining (DM) is a powerful process to extract knowledge and discover new patterns embedded in large data sets. DM has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. Among the various units of a cardiology department, Autonomic Nervous System (ANS) is one of the most important and active unit. Thus, the aim of this study is to build a decision tree-based classifier using a data set collected from an ANS unit of the Moroccan university hospital Avicenne. The decision tree construction algorithm used in this study is C4.5. The classifier obtained presented a high level of accuracy measured in terms of error rate.


  1. Aparna, R.. Bincy, G., Mathu, T., 2012. Survey on common data mining classification Technique. In International Journal of Wisdom Based Computing, 2.
  2. Apté, C., Weiss, S., 1997. Data mining with decision trees and decision rules. In Future Generation Computer Systems.
  3. Benarroch, E., 1993. The central autonomic network: Functional organization, dysfunction and perspective. In Mayo Clinic Proceedings.
  4. Breiman, L., Friedman, J., Olshen, R. A., Stone, C. J. (1984). Classification and regression trees, Chapman and Hall/CRC, 1st edition.
  5. Coghlan, H. C., 1996. Orthostatic intolerance: mitral valve prolapse. In Primer on the Autonomic Nervous System, D. Robertson, P. A. Low, and J. Polinsky, Eds., Academic Press, San Diego, Calif, USA.
  6. Esfandiari, N., Babavalian, M. R., Moghadam, A. E., Tabar, V. 2014. Knowledge discovery in medicine: Current issue and future trend. In Expert Systems with Applications.
  7. Familia, A., Shenb, W. M., Weberc, R., Simoudis, E., (1997). Data preprocessing and intelligent data analysis. In Intelligent Data Analysis.
  8. Grubb, B. P., Karas, B., 1999. Clinical Disorders of the Autonomic Nervous System Associated With Orthostatic Intolerance: An Overview of Classification, Clinical Evaluation and Management. In Pacing and Clinical Electrophysiology.
  9. Han, J., Kamber, M. 2001. Data Mining, Concepts and Techniques, Morgan Kaufmann publisher.
  10. Han, J., Kamber, M., Pei, P., 2011. Data preprocessing. In The Morgan Kaufmann Series in “Data Management Systems”, Morgan Kaufmann Publishers.
  11. Johansen, T. L., Kambskar, G., Mehlsen, J., 1997. Heart rate variability in evaluation of the autonomic nervous system. In Ugeskr Laeger.
  12. Karaolis, M. A., Moutiris, J. A., Hadjipanayi, D., Pattichis, C. S., 2010. Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees. In IEEE Transactions on Information Technology in Biomedicine.
  13. Kreibig, S. D., 2010. Autonomic nervous system activity in emotion: A review. In Biological Psychology.
  14. Kumari, M., Godara, S., 2011. Review of Data Mining Classification Models in Cardiovascular Disease Diagnosis. In Proceedings published in International Journal of Computer Applications.
  15. Langley J. N., 1921. The Autonomic Nervous System., Cambridge Heffer.
  16. Low P. A., 1997. Laboratory evaluation of autonomic function. In Clinical Autonomic Disorders. Evaluation and Managment.
  17. Mašetic, Z., Subasi, A. 2013. Detection of congestive heart failures using C4.5 Decision Tree. In Southeast Europe Journal of Soft Computing.
  18. Mejía-Rodríguez, A. R., Gaitán-González, M. J., Carrasco-Sosa, J., Guillén-Mandujano, A., 2009. Time Varying Heart Rate Variability Analysis of Active Orthostatic and Cold Face Tests Applied Both Independently and Simultaneously. In Computers in Cardiology.
  19. Pavlopoulos, S. A., Stasis, A. C., Loukis, E. N., 2004. A decision treebased method for the differential diagnosis of aortic stenosis from mitral regurgitation using heart sounds. In Biomed. Eng. OnLine.
  20. Quinlan, J. R. 1979. Discovering rules by induction from large collections of examples. In Expert systems in the micro electronic age. Edinburgh University Press.
  21. Quinlan, J. R. 1993. C4.5 Programs for Machine Learning, Morgan Kaufmann publisher.
  22. Quinlan, J. R. 1996. Improved use of continuous attributes in C4.5. In Journal of Artificial Intelligence Research.
  23. Shields R. W., 2009. Heart rate variability with deep breathing as a clinical test of cardiovagal function. In Cleveland Clinic Journal of Medicine.
  24. Witten, H. I., Frank, E. 1999. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann publisher. 1st edition.
  25. Witten, H. I., Frank, E. 2005. Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann publisher. 2nd edition.
  26. Zheng, Y., Peng, L., Lei, L., Junjie, Y., 2005. R-C4.5 Decision tree model and its applications to health care dataset. In Proc of International Conference on Services Systems and Services Management.

Paper Citation

in Harvard Style

Idri A., Kadi I. and Benjelloun H. (2015). Heart Disease Diagnosis Using C4.5 Algorithms - A Case Study . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 397-404. DOI: 10.5220/0005216403970404

in Bibtex Style

author={Ali Idri and Ilham Kadi and Halima Benjelloun},
title={Heart Disease Diagnosis Using C4.5 Algorithms - A Case Study},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},

in EndNote Style

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Heart Disease Diagnosis Using C4.5 Algorithms - A Case Study
SN - 978-989-758-068-0
AU - Idri A.
AU - Kadi I.
AU - Benjelloun H.
PY - 2015
SP - 397
EP - 404
DO - 10.5220/0005216403970404