Rule-Based Data Mining for Diagnosis of Coronary Heart Disease
Keywords:Cleveland Dataset, Coronary Heart Disease, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Variable Precision Rough Set (VPRS),
AbstractCoronary heart disease is the leading cause of human death due to the presence of plaque (fat) in the blood vessels. Electrocardiograph (ECG) and treadmill tests are commonly used for coronary disease detection. However, it is costly, at risk and sometimes the diagnosis result is not accurate. This research aims to classify coronary heart disease dataset based on two rules of data mining methods, i.e. variable precision rough set (VPRS) and repeated incremental pruning to produce error reduction (RIPPER). These rules are chosen to observe the simplest pattern of rules knowledge from big data, imprecise and ambiguous data. The proposed method is evaluated on Cleveland coronary heart disease dataset taken from the UCI repository. The combination of VPRS and RIPPER obtains the best evaluation result with accuracy achieved of 92.99%. While the accuracy of VPRS and RIPPER is merely 75.22% and 88.13%, respectively. It indicates that the proposed method successfully classifies coronary heart disease dataset and has a potential to be implemented in the development of a computerised coronary heart disease diagnosis system.
W. H. Organisation. (2017, 29 May). About cardiovascular diseases. Available: http://www.who.int/cardiovascular_diseases/about_cvd/en/
B. L. Zaret, L. S. Cohen, and M. Moser, Yale university school of medicine heart book: William Morrow and Co., 1992.
T. J. Peter and K. Somasundaram, "Study and development of novel feature selection framework for heart disease prediction," International Journal of Scientific and Research Publications, vol. 2, pp. 1-7, 2012.
J. Soni, U. Ansari, D. Sharma, and S. Soni, "Predictive data mining for medical diagnosis: An overview of heart disease prediction," International Journal of Computer Applications, vol. 17, pp. 43-48, 2011.
W. W. Cohen, "Fast effective rule induction," in Proceedings of the twelfth international conference on machine learning, 1995, pp. 115- 123.
M. Kumari and S. Godara, "Comparative study of data mining classification methods in cardiovascular disease prediction 1," 2011.
A. H. Chen, S.-Y. Huang, P.-S. Hong, C.-H. Cheng, and E.-J. Lin, "HDPS: Heart disease prediction system," in Computing in Cardiology, 2011, 2011, pp. 557-560.
W. Ziarko, "Variable precision rough set model," Journal of computer and system sciences, vol. 46, pp. 39-59, 1993.
W. Ziarko, "Probabilistic decision tables in the variable precision rough set model," Computational Intelligence, vol. 17, pp. 593-603, 2001.
N. A. Setiawan and H. A. Nugroho, "Deteksi Penyakit Jantung Koroner Menggunakan Model Variable Precision Rough Set dan Logika Fuzzy," Universitas Gadjah Mada, 2014.
B. Tripathy, D. Acharjya, and V. Cynthya, "A framework for intelligent medical diagnosis using rough set with formal concept analysis," arXiv preprint arXiv:1301.6011, 2013.
K. Bache and M. Lichman, "UCI Machine Learning Repository [http://archive. ics. uci. edu/ml]. University of California, School of Information and Computer Science," Irvine, CA, 2013.
C.-T. Su and J.-H. Hsu, "Precision parameter in the variable precision rough sets model: an application," Omega, vol. 34, pp. 149-157, 2006.
I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann, 2016.
J. Han, "Micheline kamber, 2006.“Data Mining: Concepts and Techniques," ed: Mortgan Kaufmann Publishers, 2005.
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