In health concern business, data mining plays a significant task for predicting diseases. Mining association rule is one of the interesting topics in data mining which is used to generate frequent itemsets. It was first proposed for market basket analysis. Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. This classical algorithm is inefficient due to so many scans of database. When the database is large, it will take too much time to scan the database and may produce a larger number of candidate item sets. To overcome these limitations, researchers have made a lot of improvements to the Apriori. In this paper, the authors proposed a method to predict the heart disease through frequent itemsets. Frequent itemsets are generated based on the chosen symptoms and minimum support value. The extracted frequent itemsets help the medical practitioner to make diagnostic decisions. The aim of our proposed technique is to obtain the frequent symptoms and evaluate the performance of new technique and compare with the existing classical Apriori with support count.

Apriori, Frequent Diseases, Medical Data, Fuzzy Set, Fuzzy Intersection

[1] R.Agrawal and Srikant. R “Fast Algorithms for Mining Association Rules”. In: Proceedings of 20th International Conference of Very Large Data Bases. pp. 487-499, 1994.
[2] J. Ayres and Flannick.J, Gehrke.J, and Yiu.T “ Sequential pattern mining using a bitmap representation” .In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 429-435 , 2002.
[3] Changsheng Zhang and Jing Ruan “A Modified Apriori Algorithm with its application in Instituting Cross-Selling strategies of the Retail Industry”. In: Proc. of International Conference on Electronic Commerce and Business Intelligence. pp. 515-518, 2009.
[4] J. Chen and Xiao.K “ BISC: A bitmap itemset support counting approach for efficient frequent itemset mining”. In: ACM Transactions on Knowledge Discovery from Data (TKDD). vol. 4, p. 12, 2010.
[5] Chen Chu-xiang, Shen Jian-jing, Chen Bing, Shang Chang-xing, Wang Yun-cheng “An Improvement Apriori Arithmetic based on Rough set Theory” .In: Third Pacific-Asia Conference on Circuits, Communications and System (PACCS). pp.1 - 3 , 2011.
[6] Dongme Sun and Sheohue Teng “An algorithm to improve the effectiveness of Apriori Algorithm”. In: Proc.of 6th ICE Int. Conf. on Cognitive Informatics. pp. 385-390, 2007.
[7] Feldman, Aumann.R, Amir.Y, Zilberstain.A, Kloesgen.A, Ben-Yehuda (W), Maximal association.Y “Association rules: a new tool for mining for keyword co-occurrences in document collection”. In: Proceedings of the 3rd International Conference on Knowledge Discovery. pp. 167-170, 1997.
[8] J.W.Guan, Bell, D.A, Liu, D.Y “The Rough Set Approach to Association Rule Mining”. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM’03). pp.529 – 532,2003.
[9] Hanbing Liu and Baisheng Wang “An Association Rule Mining Algorithm Based on a Boolean Matrix” In: Data Science Journal. Vol.6, supplement 9,2010.
[10] Jaisree singh, Hari Ram, Dr.J.S. “Improving efficiency of Apriori Algorithm using Transaction Reduction”. In: IJSRP. Vol. 3. ISSN 2250-3153, 2013.
[11] K.Kavitha.and Dr.E.Ramaraj) “Efficient Transaction Reduction in Actionable Pattern Mining for High Voluminous Datasets based on Bitmap and Class Labels”. In: IJCSE. Vol.5 No.07 Jul 2013. ISSN: 0975- 3397,2013.
[12] T.Logeswari, Valarmathi.N, Sangeetha. A, Masilamani.M “ Analysis of Traditional and Enhanced Apriori Algorithm in Association Rule Mining” . In: International Journal of Computer Applications. Vol.87, 2013.
[13] A. Pethalakshmi and V.Vijayalakshmi “An Efficient Count Based Transaction Reduction Approach For Mining Frequent Patterns”. In: Procedia Computer Science- Elsevier,Vol.47, pp. 52-61,2015.
[14] E.Ramaraj, K.RameshKumar, N.Venkatesan “A Better Performed Transaction Reduction Algorithm for Mining Frequent Itemsets from Large Voluminuous Database”. In: Proceeding of the 2nd National Conference, Computing for Nation Development, February 08, 2008.
[15] Sixue Bai and Xinxi Dai “An efficiency Apriori algorithm: P_matrix algorithm”. In: First International Symposium on Data, Privacy and Ecommerce. pp.101-103, 2007.
[16] WANG Guo-Yin “ Calculation Methods for Core Attributes of Decision Table[J]”. In: CHINESE JOURNAL OF COMPUTERS. Vol. 26(5): 611-615, 2003.
[17] Wanjun Yu and Xiaochun Wang “The Research of Improved Apriori Algorithm for Mining Association Rules”. In: Proc. of 11th IEEE International Conference on Communication Technology Proceedings. pp. 513-516, 2004.
[18] XIA Ying, ZHANG WANG Guo-yin. “Spatio-temporal Association Rule Mining Algorithm and its Application in Intelligent Transportation System[J]”. In: Computer Science. Vol. 38(9): 173-176, 2011.
[19] XIAO Bo, XU Qian-Fang, LIN Zhi-Qing, GUO Jun, LI Chun-Guang “Credible Association Rule and Its Mining Algorithm Based on Maximum Clique[J]”. In: Journal of Software. (10): 2597-2610, 2018.
[20] Vaibhav Jain, “Evaluating and Summarizing Students’s Feed Back Using Opinion Mining”, International Journal of Scientific Research in Computer Science and Engineering. Vol.1.Issue 1. Jan-Feb 2013.
[21] Vaibhav Jain, “Frequent Navigation Pattern Mining from Web usage ”, International Journal of Scientific Research in Computer Science and Engineering. Vol.1.Issue 1. Jan-Feb 2013.