生存机器学习方法用于预测低社会人口指数印度各州的五岁以下儿童死亡率
Mukesh Vishwakarma1, Gargi Tyagi1,2, Rehana Vanaja Radhakrishnan3
1Department of Mathematics and Statistics, Faculty of Mathematics and Computing, Banasthali Vidyapith, Rajasthan, India.
Journal of research in health sciences
|August 18, 2025
概括
数以百万计的儿童在5岁之前死亡,特别是在印度. 关键的风险因素包括年轻的孕产妇年龄,缺乏教育,贫困和低出生体重,突出了改善医疗保健和教育的需要.
科学领域:
- 公共卫生 公共卫生
- 生物统计学 生物统计学
- 人口统计学 人口统计学
背景情况:
- 全球每年都有数以百万计可预防的5岁以下儿童死亡.
- 印度的社会人口指数 (LSDI) 较低的州面临着高的五岁以下儿童死亡率,即每1000个活产儿中有45个.
- 预测儿童死亡率和识别相关因素对于干预至关重要.
研究的目的:
- 预测印度LSDI州的五岁以下儿童死亡率.
- 确定与儿童死亡率相关的主要人口和社会经济因素.
主要方法:
- 一项横截面研究分析了来自94202名儿童的国家家庭健康调查-5 (NFHS-5) 数据.
- 生存模型的比较:Cox比例危险,随机生存森林和梯度增强的生存.
- 模型性能使用一致性指数,集成的Brier分数和时间依赖的ROC曲线进行评估.
主要成果:
- 研究中的4.5%的儿童在5岁生日前死亡.
- 观察到较年轻的母亲年龄 (15-25岁),未受过教育的母亲,较差的财富指数和低出生体重的死亡风险增加.
- 随机生存森林模型在识别风险因素方面表现优越.
结论:
- 通过教育赋予妇女权力和改善计划生育至关重要.
- 解决贫困问题和确保公平获得医疗保健对于减少儿童死亡率至关重要.
- 调查结果可以为政策制定提供信息,以提高弱势群体儿童生存率.
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