揭示印度家庭级别衰老的预测因素:使用NFHS-5和卫星数据的机器学习方法
Prashant Kumar Arya1, Koyel Sur2, Tanushree Kundu3
1Institute for Human Development, Delhi, India; ICSSR Post-Doctoral Fellow, Central University of Jharkhand, Ranchi, India.
Nutrition (Burbank, Los Angeles County, Calif.)
|January 23, 2025
概括
印度的儿童发育迟缓是由贫困,家庭结构,教育和温度和植被等环境因素预测的. 机器学习模型突出了解决社会经济和环境决定因素的综合干预的需要.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 环境科学 环境科学
背景情况:
- 儿童发育迟缓影响35%的5岁以下的印度儿童,构成重大公共卫生挑战.
- 现有的预测模型缺乏整合多样化的数据源和复杂的因素相互作用.
研究的目的:
- 预测印度家庭层面的儿童发育迟缓.
- 通过机器学习整合社会经济,人口和环境数据.
主要方法:
- 采用随机森林回归,支向量机回归,K-最近邻居回归和规则化的线性回归.
- 利用了国家家庭健康调查和卫星数据集的数据.
- 随机森林回归显示出最高的预测准确性.
主要成果:
- 贫困,依赖率,家庭主管教育,平均温度和叶面积指数是重要的预测因素.
- 空间分析显示,印度中部和东部的发育迟缓的地理聚类.
- 环境变量显著影响了营养结果.
结论:
- 干预措施必须解决社会经济差距以及气候和植被等环境因素.
- 一个整体的方法是对抗儿童发育迟缓在印度至关重要的.
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