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ORIGINAL RESEARCH article

Front. Public Health
Sec. Children and Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1373883

A Cross-Sectional Study of Parental Perspectives on Children about COVID-19 and Classification Using Machine Learning Models

Provisionally accepted
Fahmida Kousar Fahmida Kousar 1Arshiya Sultana Arshiya Sultana 2Manoj Shamkuwar Manoj Shamkuwar 1Md Belal Bin Heyat Md Belal Bin Heyat 3*Mohd Ammar Bin Hayat Mohd Ammar Bin Hayat 4Eram Sayeed Eram Sayeed 4Saba Parveen Saba Parveen 5Khaleequr Rahman Khaleequr Rahman 2Marwan Albahar Marwan Albahar 5Abdullah Alammari Abdullah Alammari 5John Lira John Lira 6
  • 1 University of Delhi, New Delhi, National Capital Territory of Delhi, India
  • 2 National Institute of Unani Medicine (NIUM), Bangalore, Karnataka, India
  • 3 Westlake University, Hangzhou, China
  • 4 Jamia Hamdard University, New Delhi, National Capital Territory of Delhi, India
  • 5 Umm al-Qura University, Mecca, Saudi Arabia
  • 6 National University, Manila, Philippines

The final, formatted version of the article will be published soon.

    This study delves into the parenting cognition perspectives on COVID-19 in children, exploring symptoms, transmission modes, and protective measures. It aims to correlate these perspectives with sociodemographic factors and employ advanced machine-learning techniques for comprehensive analysis.Method: Data collection involved a semi-structured questionnaire covering parental knowledge and attitude on COVID-19 symptoms, transmission, protective measures, and government satisfaction. The analysis utilised the Generalized Linear Regression Model (GLM), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and AdaBoost (AB).The study revealed an average knowledge score of 18.02±2.9, with 43.2% and 52.9% of parents demonstrating excellent and good knowledge, respectively. News channels (85%) emerged as the primary information source. Commonly reported symptoms included cough (96.47%) and fever (95.6%). GLM analysis indicated lower awareness in rural areas (β=-0.137, P<0.001), lower attitude scores in males compared to females (β=-0.64, P=0.025), and a correlation between lower socioeconomic status and attitude scores (β=-0.048, P=0.009). The SVM classifier achieved the highest performance (66.70%) in classification tasks.This study offers valuable insights into parental attitudes towards COVID-19 in children, highlighting symptom recognition, transmission awareness, and preventive practices. Correlating these insights with sociodemographic factors underscores the need for tailored educational initiatives, particularly in rural areas, and for addressing gender and socioeconomic disparities. The efficacy of advanced analytics, exemplified by the SVM classifier, underscores the potential for informed decision-making in public health communication and targeted interventions, ultimately empowering parents to safeguard their children's well-being amidst the ongoing pandemic.

    Keywords: Public Health, Medical Machine Learning, Parents, Children health, SARS CoV-2, healthcare, Medical intelligence, kid

    Received: 20 Jan 2024; Accepted: 26 Nov 2024.

    Copyright: © 2024 Kousar, Sultana, Shamkuwar, Belal Bin Heyat, Bin Hayat, Sayeed, Parveen, Rahman, Albahar, Alammari and Lira. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Md Belal Bin Heyat, Westlake University, Hangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.