使用神经网络模型预测卢旺达5岁以下儿童的发育迟缓状况:2020年卢旺达人口和健康调查的证据
Similien Ndagijimana1, Ignace Kabano1,2, Emmanuel Masabo1,3
1African Centre of Excellence in Data Science, Kigali, Kigali, Rwanda.
F1000Research
|February 21, 2025
概括
人工神经网络 (ANN) 通过2020年的数据,有效地预测卢旺达的儿童发展迟缓. 关键预测因素包括母亲的身高和早期开始母乳养,为有针对性的公共卫生干预提供信息.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 儿科 儿科 儿科
背景情况:
- 儿童发育迟缓是卢旺达的一个重大公共卫生问题,在2020年影响5岁以下儿童的33.3%.
- 虽然机器学习被用于缩预测,但人工神经网络 (ANN) 尽管具有预测能力,但仍未得到充分利用.
研究的目的:
- 在卢旺达利用人工神经网络 (ANN) 预测儿童发育迟缓.
- 为了利用最新的2020年人口和健康调查 (DHS) 数据来预测衰退.
主要方法:
- 使用2020年DHS数据集开发了一个多层感知子 (MLP) 模型.
- 数据集被分为80%用于培训和20%用于测试和验证.
- 模型性能使用准确度,精度,回忆和AUC-ROC进行评估,并进行特征重要性分析.
主要成果:
- 该ANN模型实现了72.0%的准确性和AUC-ROC为0.84,表明了良好的预测性能.
- 母亲的身高被确定为一个关键因素,较短的母亲与衰老风险增加有关.
- 早期开始母乳养是一个重要的保护因素,降低了发育迟缓的风险.
结论:
- 在卢旺达,ANN是预测衰老和识别关键风险因素的宝贵工具.
- 结果可以指导有针对性的干预,如营养支持和营养和卫生教育.
- 这项研究提供了可用于减少低收入和中等收入国家中年增长迟缓的见解.
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