背景：在卢旺达，在过去的五年中，儿童发育迟缓的患病率略有下降，从2015年的38%到2020年的33%左右。显然，卢旺达减少儿童发育迟缓的多部门方法是否与现有的科学知识相一致。该研究旨在使用机器学习分类器检查卢旺达两年以下国家营养计划对减少发育迟缓的好处。
方法：使用来自卢旺达国土安全部2015-2020年、MEIS和LODA家庭调查的数据。通过评价预测两岁以下儿童发育迟缓的最佳方法,对五种机器学习算法进行了建模：支持向量机，Logistic回归，K-NearNeighbor,随机森林,决策树该研究估计了Cox比例风险模型的风险比，并绘制了Kaplan-Meier曲线，以比较计划受益人和非受益人之间发育迟缓的幸存者风险。Logistic回归用于确定与发育迟缓减少相关的营养计划。Precision,召回，F1得分,准确度,和曲线下面积(AUC)是用于评估每个分类器的性能以找到最佳分类器的度量。
结果：根据提供的数据，研究表明，儿童早期发展(ECD)计划(p值=0.041)，营养敏感直接支持(NSDS)计划(p值=0.03)，ubudehe类别(p值=0.000)，厕所设施（p值=0.000），产前护理（ANC）4次就诊（p值=0.002），强化混合食品（FBF）计划（p值=0.038）和疫苗接种（p值=0.04）被发现是卢旺达两名以下儿童发育迟缓减少的重要预测因素。此外,幼儿发展的受益者(p<0.0001)，营养敏感性直接支持（p=0.0055），产前护理（p=0.0343），强化混合食品（p=0.0136）和疫苗接种（p=0.0355）的发育迟缓风险低于非受益人。最后,随机森林比其他分类器表现更好，准确率为83.7%，召回分数为90.7%，F1得分87.1%,准确率为83.9%，AUC评分为82.4%。
结论：儿童早期发展（ECD）计划，接受营养敏感直接支持（NSDS）计划，关注财富最低的五分之一家庭(乌布德赫类别)，卫生设施，四次拜访医疗保健提供者，接受强化混合食品(FBF)，并接受所有必要的疫苗是什么决定了在卢旺达的17个地区中，两个地区的发育迟缓的减少。最后,与其他型号相比，随机森林被证明是最好的机器学习(ML)分类器。随机森林是预测两岁以下儿童发育迟缓减少状况的最佳分类器。
BACKGROUND: In Rwanda, the prevalence of childhood stunting has slightly decreased over the past five years, from 38% in 2015 to about 33% in 2020. It is evident whether Rwanda\'s multi-sectorial approach to reducing child stunting is consistent with the available scientific knowledge. The study was to examine the benefits of national nutrition programs on stunting reduction under two years in Rwanda using machine learning classifiers.
METHODS: Data from the Rwanda DHS 2015-2020, MEIS and LODA household survey were used. By evaluating the best method for predicting the stunting reduction status of children under two years old, the five machine learning algorithms were modelled: Support Vector Machine, Logistic Regression, K-Near Neighbor, Random Forest, and Decision Tree. The study estimated the hazard ratio for the Cox Proportional Hazard Model and drew the Kaplan-Meier curve to compare the survivor risk of being stunted between program beneficiaries and non-beneficiaries. Logistic regression was used to identify the nutrition programs related to stunting reduction. Precision, recall, F1 score, accuracy, and Area under the Curve (AUC) are the metrics that were used to evaluate each classifier\'s performance to find the best one.
RESULTS: Based on the provided data, the study revealed that the early childhood development (ECD) program (p-value = 0.041), nutrition sensitive direct support (NSDS) program (p-value = 0.03), ubudehe category (p-value = 0.000), toilet facility (p-value = 0.000), antenatal care (ANC) 4 visits (p-value = 0.002), fortified blended food (FBF) program (p-value = 0.038) and vaccination (p-value = 0.04) were found to be significant predictors of stunting reduction among under two children in Rwanda. Additionally, beneficiaries of early childhood development (p < .0001), nutrition sensitive direct support (p = 0.0055), antenatal care (p = 0.0343), Fortified Blended Food (p = 0.0136) and vaccination (p = 0.0355) had a lower risk of stunting than non-beneficiaries. Finally, Random Forest performed better than other classifiers, with precision scores of 83.7%, recall scores of 90.7%, F1 scores of 87.1%, accuracy scores of 83.9%, and AUC scores of 82.4%.
CONCLUSIONS: The early childhood development (ECD) program, receiving the nutrition sensitive direct support (NSDS) program, focusing on households with the lowest wealth quintile (ubudehe category), sanitation facilities, visiting health care providers four times, receiving fortified blended food (FBF), and receiving all necessary vaccines are what determine the stunting reduction under two among the 17 districts of Rwanda. Finally, when compared to other models, Random Forest was shown to be the best machine learning (ML) classifier. Random forest is the best classifier for predicting the stunting reduction status of children under two years old.
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