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Identifying the Important Demographic and Financial Factors Related to the Mortality Rate of COVID-19 with Data Mining Techniques

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Soft Computing in Data Science (SCDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1489))

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Abstract

The whole world has been greatly affected by the recent emergence of the COVID-19 pandemic since December 2019, and the death toll has reached millions. Thus, this problem needs to be addressed and mitigated immediately. In this study, the primary objective is to determine the factors affecting the mortality rate of COVID-19 in demographic and financial factors. This study utilised supervised learning methods with feature selection methods: filter and wrapper, to identify factors attributed significantly to the Case Fatality Ratio (CFR), a measure for mortality. The result showed that the wrapper method running K-Nearest Neighbour with the Sequential Forward Selection produced the feature subset that gave the best result. The feature selection results also suggest that the factor - household debt is the key to affecting the mortality rate of this infectious disease.

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References

  1. World Health Organization Declares COVID-19 a ‘Pandemic.’ Here’s What That Means. https://time.com/5791661/who-coronavirus-pandemic-declaration/. Accessed 05 Feb 2021

  2. Singhal, T.: A review of coronavirus disease-2019 (COVID-19). Indian J. Pediatr. 87(4), 281–286 (2020)

    Article  Google Scholar 

  3. Dhand, R., Li, J.: Coughs and sneezes: their role in transmission of respiratory viral infections, including SARS-CoV-2. Am. J. Respir. Crit. Care Med. 202(5), 651–659 (2020)

    Article  Google Scholar 

  4. Department of Operational Support. COVID-19 Frequently Asked Questions. https://www.un.org/sites/un2.un.org/files/new_dhmosh_covid-19_faq.pdf. Accessed 28 Jan 2021

  5. Hasan, S.S., et al.: Mortality in COVID-19 patients with acute respiratory distress syndrome and corticosteroids use: a systematic review and meta-analysis. Expert Rev. Respir. Med. 14(11), 1149–1163 (2020)

    Article  Google Scholar 

  6. Stojkoski, V., Utkovski, Z., Jolakoski, P., Tevdovski, D., Kocarev, L. The socioeconomic determinants of the coronavirus disease (COVID-19) pandemic. arXiv preprint arXiv:2004.07947 (2020)

  7. Goutte, S., Péran, T., Porcher, T.: The role of economic structural factors in determining pandemic mortality rates: evidence from the COVID-19 outbreak in France. Res. Int. Bus. Financ. 54, 101281 (2020)

    Google Scholar 

  8. Prakash, K.B., Imambi, S.S., Ismail, M., Kumar, T.P., Pawan, Y.N.: Analysis, prediction and evaluation of COVID-19 datasets using machine learning algorithms. Int. J. Emerg. Trends Eng. Res. 8(5), 2199–2204 (2020)

    Article  Google Scholar 

  9. Miao, J., Niu, L.: A survey on feature selection. Procedia Comput. Sci. 91, 919–926 (2016)

    Article  Google Scholar 

  10. Ferreira, A.J., Figueiredo, M.A.: Efficient feature selection filters for high-dimensional data. Pattern Recogn. Lett. 33(13), 1794–1804 (2012)

    Article  Google Scholar 

  11. Lotte, F., et al.: A review of classification algorithms for EEG-based brain–computer interfaces: a 10-year update. J. Neural Eng. 15(3), 031005 (2018)

    Google Scholar 

  12. Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining (Vol. 454). Springer, US (2012)

    Google Scholar 

  13. Nasteski, V.: An overview of the supervised machine learning methods. Horizons. b, 4, 51–62 (2017)

    Google Scholar 

  14. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2012)

    Google Scholar 

  15. World Health Organization. Estimating mortality from COVID-19. https://www.who.int/news-room/commentaries/detail/estimating-mortality-from-covid-19. Accessed 2020/11/20.

  16. Ciaburro, G.: Regression Analysis with R: Design and Develop Statistical Nodes to Identify Unique Relationships Within Data at Scale. Packt Publishing Ltd., Birmingham (2018)

    Google Scholar 

  17. Bakshy, E., et al.: AE: a domain-agnostic platform for adaptive experimentation. In: Conference on Neural Information Processing Systems, pp. 1–8 (2018)

    Google Scholar 

  18. Rose, T.C., Mason, K., Pennington, A., McHale, P., Taylor-Robinson, D.C., Barr, B.: Inequalities in COVID19 mortality related to ethnicity and socioeconomic deprivation. MedRxiv (2020)

    Google Scholar 

  19. Turunen, E., Hiilamo, H.: Health effects of indebtedness: a systematic review. BMC Public Health 14(1), 1–8 (2014)

    Article  Google Scholar 

  20. Brzoska, P., Razum, O.: Indebtedness and mortality: analysis at county and city levels in Germany. Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)) 70(7), 387–392 (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia

    Nur Sara Zainudin & Keng-Hoong Ng

  2. Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Bandar Sungai Long, 43000, Kajang, Malaysia

    Kok-Chin Khor

Authors
  1. Nur Sara Zainudin
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  2. Keng-Hoong Ng
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  3. Kok-Chin Khor
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Corresponding author

Correspondence to Keng-Hoong Ng .

Editor information

Editors and Affiliations

  1. Universiti Teknologi MARA, Shah Alam, Malaysia

    Azlinah Mohamed

  2. Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Bee Wah Yap

  3. Universiti Teknologi MARA, Shah Alam, Malaysia

    Jasni Mohamad Zain

  4. University of Tennessee, Knoxville, TN, USA

    Michael W. Berry

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Zainudin, N.S., Ng, KH., Khor, KC. (2021). Identifying the Important Demographic and Financial Factors Related to the Mortality Rate of COVID-19 with Data Mining Techniques. In: Mohamed, A., Yap, B.W., Zain, J.M., Berry, M.W. (eds) Soft Computing in Data Science. SCDS 2021. Communications in Computer and Information Science, vol 1489. Springer, Singapore. https://doi.org/10.1007/978-981-16-7334-4_18

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  • DOI: https://doi.org/10.1007/978-981-16-7334-4_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7333-7

  • Online ISBN: 978-981-16-7334-4

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