Investigating New Patterns in Symptoms of COVID-19 Patients by Association Rule Mining (ARM)
Keywords:COVID-19, hotspot, segments, association rule mining (ARM), customer behavior analysis, market basket analysis, pattern, world health organization (WHO)
Background: COVID-19 is a major public health emergency wreaking havoc on public health, happiness, and liberty of travel, as well as the worldwide economy. Scientists from all over the world are working to develop treatments and vaccines; the WHO has given emergency approval to eight vaccines from around the world. However, it is also seen that the efficiency of vaccines is not up to the mark in different age groups. COVID-19 symptoms come in many different shapes and sizes, so it’s important to learn about them as soon as possible so that medical attention and management can be easier.
Method: The GitHub Data Repository-made COVID-19 patient data is available on the internet, which is used in this investigation. We have used the association rule mining method to look for common patterns in a targeted class or segment and then look at the symptoms based on them.
Result: The result is that this study involves individuals with a median age of 52 years old. Few frequent symptoms like respiratory failure (1%), septic shock (1.4%), respiratory distress syndrome (1.8%), diarrhoea (1.8%), nausea (2%), sputum (3%), headache (5%), sore throat (8%), pneumonia (8%), weakness (7%), malaise/body pain (11%), cough (37%), fever (67%) and remaining diseases like myocardial infarction, cardiac failure, and renal illness (less than 1%) were present. If a patient had chronic disease, respiratory failure, and pneumonia, there was a higher risk of death; if a patient had a combination of chronic disease, respiratory failure, and pneumonia, respiratory failure in the age range of 45 to 84 years there was a higher risk of death. Patients having chronic conditions like pneumonia or renal disease symptoms that died as a result of the corona virus had more serious indication patterns than those without chronic diseases.
WHO, Coronavirus Disease (COVID-19) Pandemic, May 2020. Accessed: 2020-07-27.
Worldometer, COVID-19 Coronavirus Pandemic Reported Cases and Deaths by Country, Territory, or Conveyance, 2020. Accessed: 2020-07-26.
Recovery Collaborative Group, Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, Linsell L, Staplin N, Brightling C, Ustianowski A, Elmahi E, Prudon B, Green C, Felton T, Chadwick D, Rege K, Fegan C, Chappell LC, Faust SN, Jaki T, Jeffery K, Montgomery A, Rowan K, Juszczak E, Baillie JK, Haynes R, Landray MJ., Dexamethasone in hospitalized patients with covid-19-preliminary report, N. Engl. J. Med. Jul 17:NEJMoa2021436 (2020).
WHO, Solidarity Clinical Trial for COVID-19 Treatment, 2020. Accessed: 2020-07-27.
Jonathan Grein, Norio Ohmagari, Daniel Shin, Diaz George, Erika Asperges, Antonella Castagna, Torsten Feldt, Gary Green, Margaret L. Green, François-Xavier Lescure, et al., Compassionate use of remdesivir for patients with severe covid-19, N. Engl. J. Med. 382 (24) (2020) 2327–2336.
Bauchner Howard, Phil B. Fontanarosa, randomized clinical trials and covid-19: managing expectations, JAMA 323 (22) (2020) 2262–2263.
Jeff Craven, COVID-19 Therapeutic Tracker, 2020. Accessed: 2020-12-20.
K. Pazhanikumar, S. Arumugaperumal, Association rule mining and medical application: a detailed survey, Int. J. Comput. Appl. 80 (17) (2013).
Kamran Shaukat, Sana Zaheer, Iqra Nawaz, Association rule mining: an application perspective, Int. J. Contr. Syst. Instrum. (1) (2015) 29–38, 2015.
Sui-Lee Wee Carol Zimmer, Jonathan Corum, Coronavirus Vaccine Tracker, Updated on December 18, 2020, 2020. Accessed: 2020-12-20.
Bloomberg. More, than 1.6 million People Have Been Vaccinated- Covid-19 Tracker, Updated December 19, 2020, 2020. Accessed: 2020-12-20.
Kristine A. Moore, Marc Lipsitch, John M. Barry, Michael T. Osterholm, Part 1: the Future of the Covid-19 Pandemic: Lessons Learned from Pandemic Influenza. COVID-19: the CIDRAP Viewpoint, Center for Infectious Disease Research and Policy, 2020.
Gangqiang Guo, Lele Ye, Kan Pan, Yu Chen, Xing Dong, Kejing Yan, Zhiyuan Chen, Ning Ding, Wenshu Li, Hong Huang, et al., new insights of emerging sars-cov-2: epidemiology, etiology, clinical features, clinical treatment, and prevention, Front. Cell Dev. Biol. 8 (2020) 410.
Andreas, Oskar Eriksson, Martin Nordberg, Analysis of scientific publications during the early phase of the covid-19 pandemic: topic modeling study, J. Med. Internet Res. 22 (11) (2020), e21559.
A. Sayed, Y. Acharya, K.C.V. Long, L. Lynam, M. Tandan, Estimation of clinical comorbidities in covid-19 patients: a systematic re-view and meta-analysis, Ann. Microbiol. Res. 4 (1) (2020) 105–111.
Jean-Marc Adamo, Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms, Springer Science & Business Media, 2001.
Shreshth Tuli, Shikhar Tuli, Gurleen Wander, Praneet Wander, Sukhpal Singh Gill, Schahram Dustdar, Rizos Sakellariou, Omer Rana, Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions, Internet Technol. Lett. 3 (2) (2020) e145.
Adrien Depeursinge, Ann N Leung Anne S Chin, Donato Terrone, Michael Bristow, Glenn Rosen, Daniel L. Rubin, Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution ct, Invest. Radiol. 50 (4) (2015) 261.
Maxwell W. Libbrecht, William Stafford Noble, Machine learning applications in genetics and genomics, Nat. Rev. Genet. 16 (6) (2015) 321–332.
Shreshth Tuli, Shikhar Tuli, Rakesh Tuli, Sukhpal Singh Gill, Predicting the Growth and Trend of Covid-19 Pandemic Using Machine Learning and Cloud Computing, Internet of Things, 2020, p. 100222.
Mohammad Jamshidi, Lalbakhsh Ali, Jakub Talla, Zdeněk Peroutka, Farimah Hadjilooei, Pedram Lalbakhsh, Morteza Jamshidi, Luigi La Spada, Mirhamed Mirmozafari, Mojgan Dehghani, et al., Artificial intelligence and covid- 19: deep learning approaches for diagnosis and treatment, IEEE Access 8 (2020) 109581–109595.
Talha Burak Alakus, Ibrahim Turkoglu, Comparison of deep learning approaches to predict covid-19 infection, Chaos, Solit. Fractals 140 (2020) 110120.
Chunming Xu, Scott A. Jackson, Machine Learning and Complex Biological Data, 2019.
Wolfgang Huber, Vincent J. Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S. Carvalho, Hector Corrada Bravo, Sean Davis, Laurent Gatto, Thomas Girke, et al., Orchestrating high-throughput genomic analysis with bioconductor, Nat. Methods 12 (2) (2015) 115–121.
Rosanna Upstill-Goddard, Diana Eccles, Joerg Fliege, Andrew Collins, Machine learning approaches for the discovery of gene-gene interactions in disease data, Briefings Bioinf. 14 (2) (2013) 251–260.
Pokharel Suresh, Zhenkun Shi, Zuccon Guido, Li Yu, Discriminative features generation for mortality prediction in icu, in: International Conference on Advanced Data Mining and Applications, Springer, 2020.
Chandra Prasetyo Utomo, Hanna Kurniawati, Xue Li, Suresh Pokharel, Personalised medicine in critical care using bayesian reinforcement learning, in: International Conference on Advanced Data Mining and Applications, Springer, 2019, pp. 648–657.
Jessica Vamathevan, Dominic Clark, Czodrowski Paul, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, et al., Applications of machine learning in drug discovery and development, Nat. Rev. Drug Discov. 18 (6) 2019) 463–477.
Timilsina Mohan, Meera Tandan, Mathieu d’Aquin, Haixuan Yang, discovering links between side effects and drugs using a diffusion-based method, Sci. Rep. 9 (1) (2019) 1–10.
Pokharel Suresh, Zuccon Guido, Li Xue, Chandra Prasetyo Utomo, Li Yu, Temporal Tree Representation for Similarity Computation between Medical Patients. Artificial Intelligence in Medicine, 2020.
Jean-Baptiste Lamy, Boomadevi Sekar, Gilles Guezennec, Jacques Bouaud, Brigitte Séroussi, Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach, Artif. Intell. Med. 94 (2019) 42–53.
Rakesh Agarwal, Ramakrishnan Srikant, et al., Fast algorithms for mining association rules, in: Proc. Of the 20th VLDB Conference, 1994, pp. 487–499.
Jiawei Han, Jian Pei, Mining frequent patterns by pattern-growth: methodology and implications, ACM SIGKDD Explor. Newslett. 2 (2) (2000) 14–20.
https://github.com/Mtandan/COVID_ARM.git. Accessed: 2021-22-10.
Becker, U. and Fahrmeier, L. (2001) Bump hunting for risk: a new data mining tool and its applications. Computational Statistics 16 (3) 373–386.
Jesmin Nahar, Tasadduq Imam, Kevin S. Tickle, Yi-Ping Phoebe Chen, Association rule mining to detect factors which contribute to heart disease in males and females, Expert Syst. Appl. 40 (4) (2013) 1086–1093.
Stefan Mutter, Mark Hall, Eibe Frank, using classification to evaluate the output of confidence-based association rule mining, in: Australasian Joint Conference on Artificial Intelligence, Springer, 2004, pp. 538–549.
Tyler McCormick, Cynthia Rudin, David Madigan, A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction, 2011.
Laszlo Szathmary, Petko Valtchev, Amedeo Napoli, Generating Rare Association Rules Using the Minimal Rare Itemsets Family, 2010.
Coronavirus disease 2019 (COVID-19): A literature review Journal of Infection and Public Health 13(5) Harapan Harapan, Naoya Itoh, Amanda Yufika, Wira Winardi, Synat Keam, Haypheng Te, Dewi Megawati, Zinatul Hayati, Abram L. Wagner, Mudatsir Mudatsir, April 2020.
Tandan, M., Acharya, Y., Pokharel, S., & Timilsina, M. Discovering symptom patterns of COVID-19 patients using association rule mining. Computers in Biology and Medicine, 131, 104249, 2021. https://doi.org/10.1016/j.compbiomed.2021.104249.
Rui Pang, Yue Chang, An Improved HotSpot Algorithm and Its Application to Sandstorm Data in Inner Mongolia, Apr 2020.