A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models - 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 (Frontiers in Public Health)
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.