摘要
背景：发育迟缓是卢旺达一个严重的公共卫生问题，2020 年约有 33.3% 的五岁以下儿童受到影响。研究人员已采用机器学习算法来预测卢旺达的发育迟缓问题；然而，尽管ANNs预测发育迟缓的能力很强，但很少有研究使用ANNs。本研究的目的是利用方差分析和 2020 年最新的人口与健康调查数据预测卢旺达的发育迟缓情况：2020年人口与健康调查数据集用于训练和测试预测儿童发育迟缓的ANN模型。数据集包括各种儿童、父母和社会人口特征，分为 80% 的训练数据和 20% 的测试和验证数据。模型采用了多层感知器（MLP）。模型性能通过准确度、精确度、召回率和 AUC-ROC 进行评估。确定了特征重要性，并强调了发育迟缓的最关键预测因素：测试集的总体准确率为 72.0%，AUC-ROC 为 0.84，表明模型性能良好。在负值方面，导致发育迟缓的因素似乎很多。首先，母亲的身高很重要，因为身高越低，儿童发育迟缓的风险越大。另一方面，积极价值特征强调的是降低发育迟缓可能性的因素。开始母乳喂养的时间是一个关键因素，这表明尽早开始母乳喂养与降低发育迟缓的风险有关：这些研究结果表明，方差网络可以成为预测卢旺达儿童发育迟缓和确定发育迟缓最重要相关因素的有用工具。这些见解可为有针对性的干预措施提供信息，以减轻卢旺达和其他中低收入国家的发育迟缓负担。潜在的针对性干预措施包括为孕妇和哺乳期母亲提供营养支持计划，以及为父母提供营养和卫生方面的教育计划。Background: Stunting is a serious public health concern in Rwanda, affecting around 33.3% of children under five in 2020. The researchers have employed machine learning algorithms to predict stunting in Rwanda; however, few studies used ANNs, despite their strong capacity to predict stunting. The purpose of this study was to predict stunting in Rwanda using ANNs and the most recent DHS data from 2020.
Methods: DHS 2020 dataset was used to train and test an ANN model for predicting stunting in children. The dataset, which included various child, parental, and socio-demographic characteristics, was split into 80% training data and 20% testing and validation data. The model utilised a multilayer perceptron (MLP). Model performance was assessed using accuracy, precision, recall, and AUC-ROC. Feature importances were determined and highlighted the most critical predictors of stunting.
Results: An overall accuracy of 72.0% on the test set was observed, with an AUC-ROC of 0.84, indicating the model's good performance. Factors appear to contribute to stunting among the negative value aspects. First and foremost, the mother's height is important, as a lower height suggests an increased risk of stunting in children. Positive value characteristics, on the other hand, emphasise elements that reduce the likelihood of stunting. The timing of the initiation of breastfeeding stands out as a crucial factor, showing that early breastfeeding initiation has been linked with a decreased risk of stunting.
Conclusions: These findings suggest that ANNs can be a useful tool for predicting stunting in Rwanda and identifying the most important associated factors for stunting. These insights can inform targeted interventions to reduce the burden of stunting in Rwanda and other low- and middle-income countries. Potential targeted interventions include nutritional support programs for pregnant and lactating mothers, and providing educational programs for parents on nutrition and hygiene.
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