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  •   Mai Shawkat

  •   Mahmoud Badawi

  •   Ali I. Eldesouky

Abstract

The global pandemic of new coronaviruses (COVID-19) has infected many people around the world and became a worldwide concern since this disease caused illness and deaths. The vaccine and drugs are not scientifically established, but patients are recovering with antibiotic drugs, antiviral medicine, chloroquine, and vitamin C. Now it is obvious to the world that a quicker and faster solution is needed for monitoring and combating the further spread of COVID-19 worldwide, using non-clinical techniques, for example, data mining tools, enhanced intelligence, and other artificial intelligence technologies. In this paper, association rule mining is developing for the frequent itemsets discovery in COVID-19 datasets, and the extraction of effective association relations between them. This is done by demonstrates the analysis of the Coronavirus dataset by using the Apriori_Association_Rules algorithm. It involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. The major contribution of this study employment determines the effectiveness of the Apriori_Association_Rules algorithm towards a classification of medical reports. The experimental results provide evidence of the Apriori_Association_Rules algorithm regarding the execution time, memory consumption, and several associated rules that reflect its potential applications to different contexts. Therefore, the Apriori_Association_Rules algorithm will be very useful in healthcare fields to demonstrate the latest developments in medical studies fighting COVID-19.

Keywords: COVID-19; Coronavirus; Classification; data mining; pattern matching

References

Anwar, H., & Khan, "Q. U. Pathology and Therapeutics of COVID-19: A Review", International Journal of Medical Students, 2020, doi: 10.5195/ijms.2020.498.

H. Greenspan, B. van Ginneken and R. M. Summers, “Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique”, IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1153-1159,2016, doi: 10.1109/TMI.2016.2553401.

M. J. Zaki, "Scalable algorithms for association mining", IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 3, pp. 372-390,2000, doi: 10.1109/69.846291.

Webb, G. I., & Zhang, S., "Further Pruning for Efficient Association Rule Discovery", Lecture Notes in Computer Science, 2001, 2256, 605 - 618.

Pinar Yazgana, Ali Osman Kusakci, "A Literature Survey on Association Rule Mining Algorithms", Southeast Europe Journal of Soft Computing, 2016, 5. 10.21533/sc journal.v5i1.102.

Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI., "Fast discovery of association rules", Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, editors, Advances in Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press, 1996. p. 307-328.

Huan Wu, Zhigang Lu, Lin Pan, Rongsheng Xu, and Wenbao Jiang, "An improved apriori based algorithm for association rules mining", IEEE Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009, volume2, pages 51–55.

Jiao Yabing, "Research of an improved apriori algorithm in data mining association rules", International Journal of Computer and Communication Engineering,2013, 2(1):25.

Jiawei Han, Jian Pei, and Yiwen Yin, "Mining frequent patterns without candidate generation", ACM SIGMOD International Conference on Management of Data, 2001, pages 1–12.

Grahne G and Zhu J, "Fast algorithms for frequent itemset mining using FP-trees," IEEE Transactions on Knowledge and Data Engineering,2005, vol. 17, no. 10, pp. 1347-1362, Oct. 2005. doi: 10.1109/TKDE.2005.166.

Zhan, Foxiao&Zhu, Xiaolan& Zhang, Lei & Wang, Xuexi& Wang, Lu & Liu, Chaoyi, "Summary of Association Rules", IOP Conference Series: Earth and Environmental Science, 2019, doi: 10.1088/1755-1315/252/3/032219.

Anil Vasoya, Nitin Koli, “Mining of Association Rules on Large Database Using Distributed and Parallel Computing,” Procedia Computer Science, vol. 79, pp. 221-230, 2016.

J. Han, M. Kamber, "Data Mining: Concepts and Techniques, 3rd Edition", The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann,2011.

S.C. Suh, "Practical Applications of Data Mining", Jones & Bartlett Publishers,2012.

B.Venkatalakshmi, M.V Shivsankar, “Heart Disease Diagnosis Using Predictive Data Mining,” International Journal of Innovative Research in Science, Engineering and Technology, 2014.

A Kate, Rohit & Nadig, Ramya., "Stage-Specific Predictive Models for Breast Cancer Survivability", International Journal of Medical Informatics, 2016, 97. 10.1016/j.ijmedinf.2016.11.001.

A. Bellaachia and E. Guven, "Predicting breast cancer survivability using data mining techniques," Age, vol. 58, pp. 10-110, 2006.

Nagesh Shukla, Markus Hagenbuchner, Khin Than Win, Jack Yang, "Breast cancer data analysis for survivability studies and prediction", Computer Methods and Programs in Biomedicine, 2018, Volume 155.

W. Wu, H. Zhou, "Data-driven diagnosis of cervical cancer with support vector machine-based approaches", IEEE Access, 2017, vol. 5, pp. 25189–25195.

Kate, Rohit, Nadig, Ramya, "Stage-Specific Predictive Models for Breast Cancer Survivability", International Journal of Medical Informatics, 2016, doi: 97. 10.1016/j.ijmedinf.2016.11.001.

Jeffrey Dean, Sanjay Ghemawat, "Map-reduce: simplified data processing on large clusters", Conference on Symposium on operating systems Design and Implementation, 2004, pages10–10.

Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang, and Edward Y. Chang.," PFP: parallel FP-growth for query recommendation, ACM Conference on Recommender Systems, 2008, pages 107–114.

E. El-shafeyi, A. El-desouky, "A Big Data Framework for Mining Sensor Data Using Hadoop", Studies in Informatics and Control, 2017, ISSN 12201766, vol. 26(3), pp. 365-376.

Hong. Jian Qiu, RongGu, Chunfeng Yuan, and Yihua Huang." YAFIM: A parallel frequent itemset mining algorithm with a spark", Parallel and Distributed Processing Symposium Workshops, 2014, pages1664–1671.

Apache, Apache spark repository, 2016.

Feng Zhang, Min Liu, Feng Gui, Weiming Shen, Abdallah Shami, and Yunlong Ma, "A distributed frequent itemset mining algorithm using spark for big data analytics", Cluster Computing, 2015, 18(4):1493–1501.

X. Niu, M. Qian, C. Wu and A. Hou, "On a Parallel Spark Workflow for Frequent Itemset Mining Based on Array Prefix-Tree", IEEE/ACM Workflows in Support of Large-Scale Science (WORKS),2019, Denver, CO, USA, pp. 50-59.

K. D. Rajab, "New Associative Classification Method Based on Rule Pruning for Classification of Datasets", IEEE Access, 2019, vol. 7, pp. 157783157795.

Sornalakshmi, M., Balamurali, S., Venkatesulu, M. et al, "Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in the healthcare industry", Neural Comput & Applic, 2020, doi.org/10.1007/s00521-020-04862-2.

N. Karimtabar and M. J. Shayegan Fard, "An Extension of the Apriori Algorithm for Finding Frequent Items," 2020 6th International Conference on Web Research (ICWR), IEEE, 2020, pp. 330-334, doi: 10.1109/ICWR49608.2020.9122282.

Sitarski, Edward Algorithm Alley, "HATs: Hashed array trees. ", Dr. Dobb's Journal, 1996; 21 (11). http://www. ddj.com/architect/184409965?pgno=5.

https://github.com/beoutbreakprepared/nCoV2019/tree/master/covid19/data.

http://github.io/targeting2019-ncov/cov.

https://www.kaggle.com/kimjihoo/coronavirusdataset.

Eman Khashan, Ali Eldesouky, M.Fadel, Sally Elghamrawy, "A Big Data-Based Framework for Executing Complex Query Over COVID-19 Datasets (COVID-QF) ", 2020.arXiv:2005.12271.

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How to Cite
[1]
Shawkat, M., Badawi, M. and Eldesouky, A.I. 2021. A Novel Approach of Frequent Itemsets Mining for Coronavirus Disease (COVID-19). European Journal of Electrical Engineering and Computer Science. 5, 2 (Mar. 2021), 5-12. DOI:https://doi.org/10.24018/ejece.2021.5.2.304.