Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

Rahmad Kurniawan, Mohd Zakree Ahmad Nazri, Siti Norul Huda Abdullah, Jemaima Che Hamzah, Rado Yendra, Westi Oktaviana


The conventional approach in developing a rule-based expert system usually applies a tedious, lengthy and costly knowledge acquisition process. The acquisition process is known as the bottleneck in developing an expert system. Furthermore, manual knowledge acquisition can eventually lead to erroneous in decision-making and function ineffective when designing any expert system. Another dilemma among knowledge engineers are handing conflict of interest or high variance of inter and intrapersonal decisions among domain experts during knowledge elicitation stage. The aim of this research is to improve the acquisition of knowledge level using a data mining technique. This paper investigates the effectiveness of an association rule mining technique in generating new rules for an expert system. In this paper, FP-Growth is the machine learning technique that was used in acquiring rules from the eye disease diagnosis records collected from Sumatera Eye Center (SMEC) Hospital in Pekanbaru, Riau, Indonesia. The developed systems are tested with 17 cases. The ophthalmologists inspected the results from automatic rule generator for eye diseases diagnosis.  We found that the introduction of FP-Growth association rules into the eye disease knowledge-based systems, able to produce acceptable and promising eye diagnosing results approximately 88% of average accuracy rate. Based on the test results, we can conclude that Conjunctivitis and Presbyopia disease are the most dominant suffering in Indonesia. In conclusion, FP-growth association rules are very potential and capable of becoming an adequate automatic rules generator, but still has plenty of room for improvement in the context of eye disease diagnosing.


association rules; eye diseases; FP-Growth; knowledge base.

Full Text:



A. Dong and A. M. Agogino, “Text analysis for constructing design representations,†Artificial Intelligence in Engineering. vol. 11, pp. 65-75, 1997.

L. Millette, “Improving the Knowledge-Based Expert System Lifecycle,†Master's Thesis, University of North Florida, U.S., 2012.

W. Rui and L. Duo, “The study on the construction of knowledge base of grinding expert system based on data mining,†in International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), 2011, p. 845-848.

M. Golabchi, “A knowledge-based expert system for selection of appropriate structural systems for large spans,†Asian Journal of civil engineering (Building and Housing)., vol. 9, pp. 179-191, 2008.

S. Mertens, M. Rosu, and Y. Erdani, “An intelligent dialogue for online rule-based expert systems,†in Proceedings of the 9th international conference on intelligent user interfaces, 2004, p. 280-282.

W. P. Wagner, “Issues in knowledge acquisition,†in Proceedings of the 1990 ACM SIGBDP conference on Trends and directions in expert systems, 1990, p. 247-261.

R. Kurniawan, N. Yanti, and M. Z. Nazri, “Expert systems for self-diagnosing of eye diseases using Naïve Bayes,†in International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA), 2014, p. 113-116.

R. Mokhtar, N. A. M. Zin, and S. N. H. S. Abdullah, “Rule-based knowledge representation for modality learning style in AIWBES,†in Knowledge Management International Conference, 2010.

D.-L. Ma, W.-J. Zhang, B. Dong, P. Yang, and H.-X. Lu, “Establishing knowledge base of expert system with association rules,†in International Conference on Machine Learning and Cybernetics, 2008, p. 1785-1788.

M. Karabatak and M. C. Ince, “An expert system for detection of breast cancer based on association rules and neural network,†Expert systems with Applications. vol. 36, pp. 3465-3469, 2009.

S. M. Fakhrahmad, M. H. Sadreddini, and M. Zolghadri Jahromi, “A proposed expert system for word sense disambiguation: deductive ambiguity resolution based on data mining and forward chaining,†Expert Systems., vol. 32, pp. 178-191, 2015.

S.-S. Weng, S.-C. Liu, and T.-H. Wu, “Applying Bayesian network and association rule analysis for product recommendation,†International Journal of Electronic Business Management., vol. 9, pp. 149, 2011.

A. Ikram and U. Qamar, “A rule-based expert system for earthquake prediction,†Journal of Intelligent Information Systems., vol. 43, pp. 205-230, 2014.

Z. A. Othman, N. Ismail, and M. T. Latif, “Association rules of temperature towards high and low ozone in Putrajaya,†in 6th International Conference on Electrical Engineering and Informatics (ICEEI), 2017, p. 1-5.

M. F. M. Mohsin, A. A. Bakar, and M. H. A. Wahab, “A Comparative Study of Apriori and Rough Classifier for Data Mining,†Asia-Pacific Journal of Information Technology and Multimedia. vol. 5, 2008.

A. S. Saabith, E. Sundararajan, and A. A. Bakar, “Parallel implementation of apriori algorithms on the Hadoop-MapReduce platform-an evaluation of literature,†Journal of Theoretical and Applied Information Technology., vol. 85, pp. 321, 2016.

N. G. Noma and M. K. A. Ghani, “Discovering pattern in medical audiology data with FP-growth algorithm,†in Conference on Biomedical Engineering and Sciences (IECBES), 2012, p. 17-22.

A. S. Hoque, S. K. Mondal, T. M. Zaman, P. C. Barman, and M. A.-A. Bhuiyan, “Implication of association rules employing FP-growth algorithm for knowledge discovery,†in 14th International Conference Computer and Information Technology (ICCIT), 2011, p. 514-519.

J. W. Buckley, M. H. Buckley, and H.-F. Chiang, Research methodology and business decisions, 1976.

R. Kurniawan, M. Z. A. Nazri, M. Irsyad, R. Yendra, and A. Aklima, “On machine learning technique selection for classification,†in International Conference on Electrical Engineering and Informatics (ICEEI), 2015, p. 540-545.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development