A Study of Associative Classifiers with Different Rule Evaluation Measures for Tuberculosis Prediction

Artificial Intelligence Techniques - Novel Approaches & Practical Applications
© 2011 by IJCA Journal
Number 3 - Article 3
Year of Publication: 2011
Asha. T
Dr. S. Natarajan
Dr. K.N.B.Murthy

Asha. T, Dr. S Natarajan and Dr. K.N.B.Murthy. A Study of Associative Classifiers with Different Rule Evaluation Measures for Tuberculosis Prediction. IJCA Special Issue on Artificial Intelligence Techniques - Novel Approaches & Practical Applications (3):18–23, 2011. Full text available. BibTeX

	author = {Asha. T and Dr. S. Natarajan and Dr. K.N.B.Murthy},
	title = {A Study of Associative Classifiers with Different Rule Evaluation Measures for Tuberculosis Prediction},
	journal = {IJCA Special Issue on Artificial Intelligence Techniques - Novel Approaches & Practical Applications},
	year = {2011},
	number = {3},
	pages = {18--23},
	note = {Full text available}


Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium tuberculosis. It usually spreads through the air and attacks low immune bodies such as patients with Human Immunodeficiency Virus (HIV). Association Rule Mining (ARM) is one of the most popular approaches in data mining and if used in the medical domain has a great potential to improve disease prediction. This results in large number of descriptive rules. Therefore ARM can be integrated within classification task to generate a single system called as Associative classification(AC) which is a better alternative for predictive analytics. Rule evaluation plays an important role in the rule learning and classification process under Associative classification. Laplace accuracy has been widely used in algorithms such as Classification based on Predictive Association Rules (CPAR) and Predictive Rule Mining (PRM). In this paper we propose to use CPAR, PRM and First Order Inductive Learner(FOIL) with Statistical test along with Laplace accuracy as rule evaluation measures with different testing modes. We analyze the performance of these methods on TB data with two classes Pulmonary Tuberculosis(PTB) and Retroviral PTB(RPTB) that is those having TB with HIV. This approach helps in the selection of more suitable measure on a particular testing strategy. Results show that CPAR and PRM are almost same and better in accuracy and the number of rules compared to FOIL. Unfortunately when compared in terms of measures the result is same but generation time is less under statistical measure and also rule ordering differs.


  • B. Liu, W. Hsu, and Y. Ma. Integrating Classification and Association Rule Mining. In KDD ’98: Proceedings of the fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 80–86, New York, NY, USA, 1998.ACM Press.
  • A. Jorge and P. J. Azevedo. An Experiment with Association Rules and Classification: Post-Bagging and Conviction. In A. G. Hoffmann, H. Motoda, and T. Scheffer, editors, Discovery Science, volume 3735 of Lecture Notes in Computer Science, pages 137–149. Springer, 2005.
  • HIV Sentinel Surveillance and HIV Estimation, 2006. New Delhi, India: National AIDS Control Organization, Ministry of Health and Family Welfare, Government of India. http://www.nacoonline.org/Quick_Links/HIV_Data/ Accessed 06 February, 2008.
  • Orhan Er, Feyzullah Temurtas and Tantrikulu, A.C. Tuberculosis Disease Diagnosis using Artificial Neural Networks . Journal of Medical Systems, Springer, DOI= 10.1007/s10916-008-9241-x online,2008.
  • Y. W. C. Chien and Y. L. Chen. Mining Associative Classification Rules with Stock Trading Data – A GA- based Method. Knowledge-based Systems, 23(6):605-614, 2010.
  • K Kongubol, T. Rakthanmanon, and K.Waiyamai. Using Rule Order Difference Criterion to Decide Whether to Update Class Association Rules. Advances in Intelligent Information and Database Systems, 283:241-252, 2010.
  • T. D. Do, S.C. Hui, and A. C. M. Fong. Associative Classification with Artificial Immune System. IEEE Transactions on Evolutionary Computation 13(2):217-228,2009.
  • Z. Tang and Q. Liao. A New Class-based Associative Classification Algorithm. International Journal of Applied Mathematics, 2007.
  • Li, W., Han, J. and Pei, J. CMAR: Accurate and Efficient Classification based on Multiple Class-Association Rules. In proceedings of IEEE International Conference on Data Mining (ICDM’01). IEEE computer society, Washington, DC, USA, 369–376.2001.
  • Thabtah, F. A Review of Associative Classification Mining. Journal of Knowl. Eng. Rev., 2(1), 37–65.2007.
  • F. A. Thabtah, P. Cowling and Y. Peng. Multiple Labels Associative Classification. Knowledge and Information Systems, 9(1):109-129, 2006.
  • Nada Lavrac, Peter Flach, and Blaz Zupan. Rule Evaluation Measures: A Unifying View. ILP-99, LNAI 1634, pp.174-185,Springer-Verlag Berlin Heidelberg 1999.
  • Kesari Verma and O. P. Vyas. Classification Based On Calendar Based Temporal Association Rule. ADIT Journal Of Engineering, VOL. 2, NO.1, December 2005.
  • Naderi Dehkordi, M. H. Shenassa. CLoPAR: Classification based on Predictive Association Rules. In Proceedings of 3rd International IEEE Conference Intelligent Systems. September 2006.
  • NIU Qiang, XIA Shi-Xiong, ZHANG Lei. Association Classification based on Compactness of Rules. In Proceedings of Second International Workshop on Knowledge Discovery and Data Mining (WKDD). Moscow, 245-247, 2009.
  • J. Han and M. Kamber. Data mining: Concepts and Techniques: Morgan Kaufmann Pub, 2006.
  • I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition: Morgan Kaufmann Pub, 2005.