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使用机器学习技术,研究巴勒斯坦儿童和青少年营养摄入量与粮食不安全之间的关联

Radwan Qasrawi^1,2, Sabri Sgahir³, Maysaa Nemer⁴

¹Department of Computer Sciences, Al-Quds University, Jerusalem P.O. Box 20002, Palestine.

Children (Basel, Switzerland)

|June 27, 2024

概括

粮食不安全影响全球儿童,特别是在低收入国家. 这项研究将巴勒斯坦儿童的粮食不安全与营养摄入不足和社会经济因素联系在一起,突出了迫切的公共卫生需求.

科学领域:

公共卫生公共卫生
营养科学营养科学
机器学习应用机器学习应用

背景情况:

粮食不安全是影响儿童的重大全球公共卫生问题.
低收入和中等收入国家面临着儿童粮食不安全的不成比例的负担.
了解粮食不安全和营养摄入之间的联系对于有针对性的干预至关重要.

研究的目的:

采用机器学习来识别5-18岁儿童的粮食不安全和营养摄入量之间的关联.
分析社会人口统计学因素与粮食不安全之间的关系.
调查研究人口中与粮食不安全相关的营养缺乏.

主要方法:

利用机器学习算法分析了约旦河西岸1040名儿童的数据,巴勒斯坦 (2022年).
评估了粮食不安全的流行情况及其与营养摄入量和社会人口统计学变量之间的相关性.
在粮食不安全的儿童中,特定的特定营养素的消耗不足,低于建议的饮食补贴.

主要成果:

18.2%的儿童经历了粮食不安全,这与社会人口因素 (年龄,性别,收入,地理位置) 有关.
蛋白质,维生素A,B1,B5,B12,C,纤维,和铜的摄入不足与粮食不安全之间存在很强的相关性.
难民营里的儿童出现了明显更高的粮食不安全率.

结论:

儿童的粮食不安全是复杂的,受社会经济地位和位置的影响.
在粮食不安全的儿童中,营养缺乏是普遍存在的,需要量身定制的干预措施.
解决营养差距和社会经济决定因素对于改善儿童健康和福祉至关重要.

关键词:

孩子们的孩子们的孩子们的孩子们粮食不安全问题粮食不安全问题机器学习是机器学习.营养素摄入量营养素摄入量社会人口学因素社会人口学因素社会经济差异和社会经济差异

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