Frontiers in public health · 2025
A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models.
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Kousar Fahmida, Sultana Arshiya, Albahar Marwan Ali, Shamkuwar Manoj, Heyat Md Belal Bin, Hayat Mohd Ammar Bin, Parveen Saba, Lira John Irish G, Rahman Khaleequr, Alammari Abdullah, Sayeed Eram
Аннотация
BACKGROUND AND OBJECTIVE: 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 Generalised Linear Regression Model (GLM), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and AdaBoost (AB).
RESULTS: 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.
CONCLUSION: 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.
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 Generalised Linear Regression Model (GLM), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and AdaBoost (AB).
RESULTS: 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.
CONCLUSION: 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.