Qasrawi Radwan, Sgahir Sabri, Nemer Maysaa, Halaikah Mousa, Badrasawi Manal, Amro Malak, Vicuna Polo Stephanny, Abu Al-Halawa Diala, Mujahed Doa'a, Nasreddine Lara, Elmadfa Ibrahim, Atari Siham, Al-Jawaldeh Ayoub
Department of Computer Sciences, Al Quds University, Jerusalem P.O. Box 20002, Palestine.
Department of Computer Engineering, Istinye University, 34010 Istanbul, Turkey.
Children (Basel). 2024 Jul 2;11(7):810. doi: 10.3390/children11070810.
Food insecurity significantly impacts children's health, affecting their development across cognitive, physical, and socio-emotional dimensions. This study explores the impact of food insecurity among children aged 6 months to 5 years, focusing on nutrient intake and its relationship with various forms of malnutrition.
Utilizing machine learning algorithms, this study analyzed data from 819 children in the West Bank to investigate sociodemographic and health factors associated with food insecurity and its effects on nutritional status. The average age of the children was 33 months, with 52% boys and 48% girls.
The analysis revealed that 18.1% of children faced food insecurity, with household education, family income, locality, district, and age emerging as significant determinants. Children from food-insecure environments exhibited lower average weight, height, and mid-upper arm circumference compared to their food-secure counterparts, indicating a direct correlation between food insecurity and reduced nutritional and growth metrics. Moreover, the machine learning models observed vitamin B1 as a key indicator of all forms of malnutrition, alongside vitamin K1, vitamin A, and zinc. Specific nutrients like choline in the "underweight" category and carbohydrates in the "wasting" category were identified as unique nutritional priorities.
This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking.
粮食不安全对儿童健康有重大影响，影响他们在认知、身体和社会情感等方面的发育。本研究探讨6个月至5岁儿童粮食不安全的影响，重点关注营养摄入及其与各种形式营养不良的关系。
本研究利用机器学习算法，分析了约旦河西岸819名儿童的数据，以调查与粮食不安全相关的社会人口和健康因素及其对营养状况的影响。儿童的平均年龄为33个月，其中52%为男孩，48%为女孩。
分析显示，18.1%的儿童面临粮食不安全，家庭教育、家庭收入、所在地、地区和年龄是重要的决定因素。与粮食安全的儿童相比，来自粮食不安全环境的儿童平均体重、身高和上臂中部周长较低，这表明粮食不安全与营养和生长指标下降之间存在直接关联。此外，机器学习模型观察到维生素B1是所有形式营养不良的关键指标，还有维生素K1、维生素A和锌。在“体重不足”类别中，胆碱等特定营养素以及在“消瘦”类别中的碳水化合物被确定为独特的营养重点。
本研究深入了解了儿童生长问题的不同风险，为有针对性的干预措施和政策制定提供了有价值的信息。
