背景:在发展中国家,五岁以下儿童死亡率仍然是一个重要的公共卫生问题。这项研究旨在评估各种机器学习算法在预测尼日利亚五岁以下儿童死亡率方面的有效性,并确定最相关的预测因素。
方法:该研究使用了2018年尼日利亚人口与健康调查的全国代表性数据。该研究评估了人工神经网络等机器学习模型的性能,k-最近的邻居,支持向量机,朴素贝叶斯,随机森林,和使用真阳性率的Logistic回归,假阳性率,准确度,精度,F-measure,马修的相关系数,以及接收器操作特性下的区域。
结果:研究发现,机器学习模型可以准确预测五岁以下儿童的死亡率,随着随机森林和人工神经网络算法成为最佳模型,两者均达到89.47%的准确度和96%的AUROC。结果显示,5岁以下儿童死亡率在不同的特征上有显著差异,财富指数,母亲教育,产前就诊,交货地点,妇女的就业状况,曾经出生的孩子的数量,和地区被发现是尼日利亚五岁以下儿童死亡率的最高决定因素。
结论:研究结果表明,机器学习模型可用于预测尼日利亚的U5M,具有很高的准确性。该研究强调了解决社会问题的重要性,经济,以及尼日利亚人口之间的人口差异。该研究的发现可以告知政策制定者和卫生工作者制定有针对性的干预措施,以降低尼日利亚5岁以下儿童的死亡率。
BACKGROUND: Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors.
METHODS: The study used nationally representative data from the 2018 Nigeria Demographic and Health Survey. The study evaluated the performance of the machine learning models such as the artificial neural network, k-nearest neighbourhood, Support Vector Machine, Naïve Bayes, Random Forest, and Logistic Regression using the true positive rate, false positive rate, accuracy, precision, F-measure, Matthew\'s correlation coefficient, and the Area Under the Receiver Operating Characteristics.
RESULTS: The study found that machine learning models can accurately predict under-five mortality, with the Random Forest and Artificial Neural Network algorithms emerging as the best models, both achieving an accuracy of 89.47% and an AUROC of 96%. The results show that under-five mortality rates vary significantly across different characteristics, with wealth index, maternal education, antenatal visits, place of delivery, employment status of the woman, number of children ever born, and region found to be the top determinants of under-five mortality in Nigeria.
CONCLUSIONS: The findings suggest that machine learning models can be useful in predicting U5M in Nigeria with high accuracy. The study emphasizes the importance of addressing social, economic, and demographic disparities among the population in Nigeria. The study\'s findings can inform policymakers and health workers about developing targeted interventions to reduce under-five mortality in Nigeria.