Association rule mining for prediction of COVID-19


  • Vishnu Kumar Rai Department of Production Engineering, Jadavpur University, Kolkata, West Bengal, India
  • Santonab Chakraborty Industrial Engineering and Management Department, Maulana Abul Kalam Azad University of Technology, West Bengal, India
  • Shankar Chakraborty Department of Production Engineering, Jadavpur University, Kolkata, West Bengal, India



COVID-19, Association rule mining, Frequent pattern growth, Prediction, Regression


COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. The catastrophic shock of COVID-19 in India is also enormous. Currently, India has the largest number of COVID cases in Asia. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Based on a large clinical dataset, a linear regression model is also proposed having an accuracy of 73.9% in correctly predicting the occurrence of COVID-19.


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Aiswarya, P., Bhanu Sridhar, M., & Kavitha, L. (2020). Detection and prediction of frequent diseases in India through association technique using apriori algorithm and random forest regression. International Journal of Engineering Research & Technology, 9(3), 386-393.

Alaiad, A., Najadat, H., Mohsen, B., & Balhaf, K. (2020). Classification and association rule mining technique for predicting chronic kidney disease. Journal of Information & Knowledge Management, 19(1), 2040015.

Hareendran, S., & Chandra, S.S. (2017). Association rule mining in healthcare analytics. In: Data Mining and Big Data. Tan, Y. et al. (eds), Springer International Publishing, 31-39.

Brossette, S.E., Sprague, A.P., Hardin, J.M., Waites, K.B., Jones, W.T., & Moser, S.A. (1998). Association rules and data mining in hospital infection control and public health surveillance. Journal of the American Medical Informatics Association, 5(4), 373-381.

Burvin, J.S., & Dhanalakshmi, K. (2018). Pandemic disease detection and prevention system using mining with graph-based approach. International Journal of Pure and Applied Mathematics, 118(20), 4355-4360.

Çelik, A. (2020). Using apriori data mining method in COVID-19 diagnosis. Journal of Engineering Technology and Applied Sciences, 5(3), 121-131.

Cheng, C-W., & Wang, M.D. (2017). Healthcare data mining, association rule mining, and applications. In: Health Informatics Data Analysis. Health Information Science, Xu, D., Wang, M., Zhou, F., & Cai, Y. (eds), Springer, Cham, 201-210.

Domadiya, N., & Rao, U.P. (2018). Privacy-preserving association rule mining for horizontally partitioned healthcare data: a case study on the heart diseases. Sadhana, 43, 127-141.

Downs, S., & Wallace, M. (2000). Mining association rules from a pediatric primary care decision support system. In: Proceeding of the Annual Symposium of American Medical Informatics Association, Los Angeles, USA, 200-204.

Freeda, D.S. & Florence, M.L. (2017). An overview of disease analysis using association rule mining. International Journal of Scientific & Engineering Research, 8(4), 113-117.

Jahangir, I., Abdul, B., Hannan, A., & Javed, S. (2018). Prediction of dengue disease through data mining by using modified apriori algorithm. In: Proceedings of the 4th ACM International Conference of Computing for Engineering and Sciences, Kuala Lumpur, 1-4.

Jain, D., & Gautam, S. (2013). Implementation of apriori algorithm in health care sector: A survey. International Journal of Computer Science and Communication Engineering, 2(4), 26-32.

Jamsheela, O. (2021). Analysis of association among various attributes in medical data of heart patients by using data mining methods. International Journal of Applied Science and Engineering, 18(2), 2020215.

Kaur, J., &, Madan, N. (2015). Association rule mining: A survey. International Journal of Hybrid Information Technology, 8(7), 239-242.

Kulkarni, A.R., & Mundhe, S.D. (2017). Data mining technique: An implementation of association rule mining in healthcare. International Advanced Research Journal in Science, Engineering and Technology, 4(7), 62-65.

Lakshmi, K.S., & Vadivu, G. (2017). Extracting association rules from medical health records using multi-criteria decision analysis. Procedia Computer Science, 115, 290-295.

Ordonez, C., Ezquerra, N., & Santana, C.A. (2006). Constraining and summarizing association rules in medical data. Knowledge and Information Systems, 9(3), 259-283.

Prithiviraj, P., & Porkodi, R. (2015). A comparative analysis of association rule mining algorithms in data mining: A study. American Journal of Computer Science and Engineering Survey, 3(1), 1-10.

Sabthami, J., Thirumoorthy, K., & Muneeswaran, K. (2016). Mining association rules for early diagnosis of diseases from electronic health records. Middle-East Journal of Scientific Research, 24, 248-253.

Said, I.U., Adam, A.H., & Garko, A.B. (2015). Association rule mining on medical data to predict heart disease. International Journal of Science Technology and Management, 4(8), 26-35.

Sambasiva Rao, P., & Uma Devi, T. (2017). Applicability of apriori based association rules on medical data. International Journal of Applied Engineering Research, 12(20), 9451-9458.

Sarıyer, G., & Taşar, C. Ö. (2020). Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining. Health Informatics Journal, 26(2), 1177-1193.

Sengupta, D., Sood, M., Vijayvargia, P., Hota, S., & Naik, P.K. (2013). Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor. Bioinformation, 9(1), 555-559.

Shawkat, M., Badawy, M., & 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), 5-12.

Stilou, S., Bamidis, P.D., Maglaveras, N., & Pappas, C. (2001). Mining association rules from clinical databases: An intelligent diagnostic process in healthcare. In: Studies in Health Technology and Informatics, IOP Press, 84, 1399-1403.

Tandan, M., Acharya, Y., Pokharel., S., & Timilsina, M. (2021). Discovering symptom patterns of COVID-19 patients using association rule mining. Computers in Biology and Medicine, 131, 104249.

Thamer, M., El-Sappagh, S., & El-Shishtawy, T. (2020). A semantic approach for extracting medical association rules. International Journal of Intelligent Engineering & Systems, 13(3), 280-292.



How to Cite

Rai, V. K. ., Chakraborty, S., & Chakraborty, S. (2023). Association rule mining for prediction of COVID-19. Decision Making: Applications in Management and Engineering, 6(1), 365–378.