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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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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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