A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models
Background and objectiveThis 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 comp...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1373883/full |
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| author | Fahmida Kousar Arshiya Sultana Marwan Ali Albahar Manoj Shamkuwar Md Belal Bin Heyat Mohd Ammar Bin Hayat Saba Parveen John Irish G. Lira John Irish G. Lira Khaleequr Rahman Abdullah Alammari Eram Sayeed |
| author_facet | Fahmida Kousar Arshiya Sultana Marwan Ali Albahar Manoj Shamkuwar Md Belal Bin Heyat Mohd Ammar Bin Hayat Saba Parveen John Irish G. Lira John Irish G. Lira Khaleequr Rahman Abdullah Alammari Eram Sayeed |
| author_sort | Fahmida Kousar |
| collection | DOAJ |
| description | Background and objectiveThis 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.MethodData 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 Generalised Linear Regression Model (GLM), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and AdaBoost (AB).ResultsThe 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.ConclusionThis 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. |
| format | Article |
| id | doaj-art-23a969b8853e471aa5d65804797fe652 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-23a969b8853e471aa5d65804797fe6522025-01-15T14:50:25ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011210.3389/fpubh.2024.13738831373883A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning modelsFahmida Kousar0Arshiya Sultana1Marwan Ali Albahar2Manoj Shamkuwar3Md Belal Bin Heyat4Mohd Ammar Bin Hayat5Saba Parveen6John Irish G. Lira7John Irish G. Lira8Khaleequr Rahman9Abdullah Alammari10Eram Sayeed11Department of Amraze Atfal, A and U Tibbia College & Hospital, Delhi University, New Delhi, IndiaDepartment of Ilmul Qabalat wa Amraze Niswan, National Institute of Unani Medicine, Ministry of AYUSH, Bengaluru, Karnataka, IndiaComputer Science Department, Umm Al-Qura University, Mecca, Saudi ArabiaDepartment of Panchkarma, A and U Tibbia College & Hospital, Delhi University, New Delhi, IndiaCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen, ChinaNational University Manila, Manila, PhilippinesDasmarinas Graduate School, De La Salle University, Dasmarinas, Cavite, Philippines0Department of Ilmul Saidla, National Institute of Unani Medicine, Ministry of AYUSH, Government of India, Bengaluru, Karnataka, India1Faculty of Education, Curriculums and Teaching Department, Umm Al-Qura University, Makkah, Saudi Arabia2Triveni Rai Kisan Mahila Mahavidyalaya, D. D. U. Gorakhpur University, Kushinagar, IndiaBackground and objectiveThis 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.MethodData 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 Generalised Linear Regression Model (GLM), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and AdaBoost (AB).ResultsThe 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.ConclusionThis 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.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1373883/fullpublic healthmedical machine learningparentschildren healthSARS CoV-2healthcare |
| spellingShingle | Fahmida Kousar Arshiya Sultana Marwan Ali Albahar Manoj Shamkuwar Md Belal Bin Heyat Mohd Ammar Bin Hayat Saba Parveen John Irish G. Lira John Irish G. Lira Khaleequr Rahman Abdullah Alammari Eram Sayeed A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models Frontiers in Public Health public health medical machine learning parents children health SARS CoV-2 healthcare |
| title | A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models |
| title_full | A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models |
| title_fullStr | A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models |
| title_full_unstemmed | A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models |
| title_short | A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models |
| title_sort | cross sectional study of parental perspectives on children about covid 19 and classification using machine learning models |
| topic | public health medical machine learning parents children health SARS CoV-2 healthcare |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1373883/full |
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