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Predicting the mental health of rural Bangladeshi children in coronavirus disease 2019

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 12 No. 5 (2022)

DOI
10.11591/ijece.v12i5.pp5501-5510
ISSN
2722-2578, 2088-8708
Published
2022-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The novel coronavirus disease 2019 (COVID-19) current pandemic is a worldwide health emergency like no other. It is not the only COVID-19 infection in infants, children, and adolescents that is causing concern among their families and professionals; there are also other serious issues that must be carefully detected and addressed. Major things are identified due to COVID-19, some elements are affecting children’s healthcare in direct or indirect ways, affecting them not just from a medical standpoint but also from social, psychological, economic, and educational perspectives. All these factors may have affected children’s mental development, particularly in rural settings. As Bangladesh faces a major challenge such as a lack of public mental health facilities, especially in rural areas. So, we discovered a method to predict the mental development condition of rural children that they are facing at this time of COVID-19 using machine learning technology. This research work can predict whether a rural child is mentally developed or mentally hampered in Bangladesh and this prediction gives nice feedback.

Collaboration network of the authors

36 authors · 138 co-authorship links · 18 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Daffodil International University
  • Notre Dame University Bangladesh
  • University of Notre Dame
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mohammad Monirul IslamDaffodil International University5
Asma MariamDaffodil International University5
Masuma ParvinDaffodil International University4
Imrus SalehinDaffodil International University3
Md. Mehedi HasanDaffodil International University3
Iftakhar Mohammad TalhaDaffodil International University3
Mohd. SaifuzzamanDaffodil International University2
Susanta Chandra DebnathDaffodil International University2
Lamisha Haque PoushyDaffodil International University2
Salauddin Ahmed BhuiyanDaffodil International University2
Jarin NooderDaffodil International University2
Ashrarfi MahbubaDaffodil International University2
Aminul HaqueDaffodil International University2
Atik Asif Khan AkashDaffodil International University2
Nusrat NabiDaffodil International University2
Farnaz Narin Nur HasanAffiliation unknown1
Md. Azizul HakimAffiliation unknown1
A S M Farhan Al HaqueDaffodil International University1
Md. Mizanur RahmanDaffodil International University1
Mahimul Islam NadimDaffodil International University1
Mahinur AktherDaffodil International University1
Ahad UllahDaffodil International University1
Jakaria AhmedDaffodil International University1
Muhammad Jalal Uddin AhmedDaffodil International University1
Raisa FabihaNotre Dame University Bangladesh1
Stein Joachim ReberioNotre Dame University Bangladesh1
Zubayer FaraziNotre Dame University Bangladesh1
Shaheena SultanaNotre Dame University Bangladesh1
A. H. M. Saiful IslamAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Predicting the mental health of rural Bangladeshi children in coronavirus disease 2019</dc:title><dc:creator>Moon, Nazmun Nessa</dc:creator><dc:creator>Hossain, Refath Ara</dc:creator><dc:creator>Jahan, Israt</dc:creator><dc:creator>Shakil, Shahriar</dc:creator><dc:creator>Uddin, Shihab</dc:creator><dc:creator>Hassan, Mahmudul</dc:creator><dc:creator>Nur, Fernaz Narin</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span&amp;gt;The novel coronavirus disease 2019 (COVID-19) current pandemic is a worldwide health emergency like no other. It is not the only COVID-19 infection in infants, children, and adolescents that is causing concern among their families and professionals; there are also other serious issues that must be carefully detected and addressed. Major things are identified due to COVID-19, some elements are affecting children’s healthcare in direct or indirect ways, affecting them not just from a medical standpoint but also from social, psychological, economic, and educational perspectives. All these factors may have affected children’s mental development, particularly in rural settings. As Bangladesh faces a major challenge such as a lack of public mental health facilities, especially in rural areas. So, we discovered a method to predict the mental development condition of rural children that they are facing at this time of COVID-19 using machine learning technology. This research work can predict whether a rural child is mentally developed or mentally hampered in Bangladesh and this prediction gives nice feedback.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2022-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v12i5.pp5501-5510</dc:identifier><dc:identifier>10.11591/ijece.v12i5.pp5501-5510</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 12 No 5 (2022); 5501</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/27098/15978</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>