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HuDA_COVID Human Disposition Analysis During COVID-19 Using Machine Learning

Author

Listed:
  • Charu Gupta

    (Bhagwan Parshuram Institute of Technology, Delhi, India)

  • Dev Gaur

    (Bhagwan Parshuram Institute of Technology, Delhi, India)

  • Prateek Agrawal

    (Lovely Professional University, Punjab, India)

  • Deepali Virmani

    (Department of Computer Science, Vivekanand Institute of Professional Studies Technical Campus,, Delhi, India)

Abstract

Coronavirus has greatly impacted various aspects of human life, including human psychology & human disposition. In this paper, we attempted to analyze the impact of the COVID-19 pandemic on human health. We propose Human Disposition Analysis during COVID-19 using machine learning (HuDA_COVID), where factors such as age, employment, addiction, stress level are studied for human disposition analysis. A mass survey is conducted on individuals of various age groups, regions & professions, and the methodology achieved varied accuracy ranges of 87.5% to 98%. The study shows people are worried about lockdown, work & relationships. Furthermore, 23% of the respondents have not had any effect. 45% and 32% have had positive and negative effects, respectively. It is a novel study in human disposition analysis in COVID-19 where a novel weighted assignment indicating the health status is also proposed. HuDA_COVID clearly indicates a need for a methodical approach towards the human psychological needs to help the social organizations formulating holistic interventions for affected individuals.

Suggested Citation

  • Charu Gupta & Dev Gaur & Prateek Agrawal & Deepali Virmani, 2021. "HuDA_COVID Human Disposition Analysis During COVID-19 Using Machine Learning," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(2), pages 1-15, July.
    • Handle: RePEc:igg:jehmc0:v:13:y:2021:i:2:p:1-15
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    References listed on IDEAS

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    1. Yingfei Zhang & Zheng Feei Ma, 2020. "Impact of the COVID-19 Pandemic on Mental Health and Quality of Life among Local Residents in Liaoning Province, China: A Cross-Sectional Study," IJERPH, MDPI, vol. 17(7), pages 1-12, March.
    2. Paolo Roma & Merylin Monaro & Laura Muzi & Marco Colasanti & Eleonora Ricci & Silvia Biondi & Christian Napoli & Stefano Ferracuti & Cristina Mazza, 2020. "How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms," IJERPH, MDPI, vol. 17(19), pages 1-17, October.
    3. Sijia Li & Yilin Wang & Jia Xue & Nan Zhao & Tingshao Zhu, 2020. "The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users," IJERPH, MDPI, vol. 17(6), pages 1-9, March.
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