对父母对孩子关于COVID-19的观点和使用机器学习模型进行分类的横截面研究
Fahmida Kousar1, Arshiya Sultana2, Marwan Ali Albahar3
1Department of Amraze Atfal, A and U Tibbia College & Hospital, Delhi University, New Delhi, India.
Frontiers in public health
|January 30, 2025
概括
家长的COVID-19知识通常很好,但对症状和保护措施的认识因地点和人口统计学而异. 机器学习模型,如SVM,可以帮助识别有针对性的健康干预的风险群体.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 儿科 儿科 儿科
背景情况:
- 父母的认知显著影响孩子的健康结果,特别是在流行病期间.
- 了解父母对COVID-19的看法对于有效的公共卫生策略至关重要.
- 社会人口统计学因素可能会影响父母对传染病的知识和态度.
研究的目的:
- 探索有关儿童COVID-19症状,传播和保护措施的家长认知.
- 为了将父母的观点与社会人口统计学因素相关联.
- 应用机器学习技术来分析家长的COVID-19知识和态度.
主要方法:
- 半结构化问卷收集了关于家长对COVID-19的知识和态度的数据.
- 统计分析包括通用线性回归模型 (GLM).
- 机器学习模型如K-最近邻居 (KNN),支持矢量机器 (SVM),随机森林 (RF),天真贝斯 (NB) 和AdaBoost (AB) 被使用.
主要成果:
- 平均家长知识分数为18.02±2.9,超过96%的人识别咳和发烧.
- 新闻频道是主要的信息来源 (85%).
- 在农村地区,男性和社会经济地位较低的人群中发现意识较低. SVM实现了66.70%的分类性能.
结论:
- 父母对COVID-19的知识很大,但需要针对农村,男性和较低社会经济群体进行有针对性的干预.
- 症状识别和保护措施意识是公共卫生传播的关键领域.
- 像SVM这样的先进分析可以增强公共卫生决策和儿童福祉干预措施.
关键词:
这就是SARS CoV-2病毒.儿童健康 儿童健康 儿童健康医疗保健 医疗保健 医疗保健 医疗保健孩子的孩子孩子的孩子医疗情报 医疗情报 医疗情报医学机器学习 医学机器学习父母 父母 父母 父母 父母 父母 父母 父母 父母 父母 父母 父母 父母公共卫生公共卫生.更多相关视频
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