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A Machine Learning-Based Approach for Classifying Socially Isolated Individuals in a Pandemic Context

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Inventive Communication and Computational Technologies (ICICCT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 757))

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Abstract

Social isolation has become a concerning factor after the COVID-19 pandemic, as the lockdown periods significantly restricted people’s interaction with the outside world. Existing studies have shown its correlation with a person’s mental and physical health. This paper focuses on identifying socially isolated individuals based on specific criteria, such as gender, family or self-earning, online or offline interactions, taking care of physical and mental health, and a couple more. The questionnaire also contains universally used PHQ9, K6, and UCLA LS3 scales to test individuals for depression, distress, and feeling of loneliness. All these input parameters were analyzed, combinations of these parameters were evaluated, and then the correlated parameters were used to prepare a machine learning-based web application that could asses an individual’s sense of feeling unwanted or socially isolated. Five supervised machine learning algorithms were employed, and it was found that the logistic regression model gave the best performance (73.6% accuracy). This study also features a web application that shows the successful implementation of our proposed approach.

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Acknowledgements

The authors are grateful to Kazi Shawpnil from United International University for her input, detailed comments, and help with paper formatting that contributed to improving this paper.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh

    Md Ulfat Tahsin, Sarah Jasim & Intisar Tahmid Naheen

Authors
  1. Md Ulfat Tahsin
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  2. Sarah Jasim
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  3. Intisar Tahmid Naheen
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Corresponding author

Correspondence to Md Ulfat Tahsin .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

    G. Ranganathan

  2. Department of Computer Science (HUMAIN-Lab), International Hellenic University, Thessaloniki, Greece

    George A. Papakostas

  3. Information Systems and Operations Management (ISEG), University of Lisbon, Lisboa, Portugal

    Álvaro Rocha

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Tahsin, M.U., Jasim, S., Naheen, I.T. (2023). A Machine Learning-Based Approach for Classifying Socially Isolated Individuals in a Pandemic Context. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_22

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