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预测和识别与加纳五岁以下儿童营养不良相关的因素,使用机器学习算法

Eric Komla Anku¹, Henry Ofori Duah²

¹Dietherapy and Nutrition, Cape Coast Teaching Hospital, Cape Coast, Ghana.

|February 13, 2024

概括

机器学习准确地预测了加纳儿童营养不良的情况. XGBoost模型在识别年龄和性别等关键因素方面表现出色,用于针对性干预,以对抗五岁以下儿童的衰减,衰减和体重不足.

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:

儿童营养不良是一个关键的全球卫生问题,特别是在发展中国家.
机器学习 (ML) 提供了预测营养不良及其决定因素的潜力.

研究的目的:

采用ML算法来预测五岁以下儿童的营养不良 (衰退,衰减,体重不足).
确定与儿童营养不良相关的重要预测因素.

主要方法:

使用R和Python进行的2017年多指标集群调查 (MICS) 的二次数据分析.
训练并评估了七个ML算法:LDA,后勤回归,SVM,RF,LASSO,Ridge和XGBoost.
使用精度,混矩阵和ROC AUC评估模型性能.

主要成果:

XGBoost以98%的准确度表现出卓越的性能,用于减肥,衰老和体重不足.
XGBoost在浪费和衰退方面实现了100%的AUC,这表明其具有很高的预测能力.
发现的关键预测因素包括年龄,体重,身高,性别,地区和种族.

结论:

在加纳,XGBoost模型对预测儿童营养不良非常有效.
机器学习算法可以有效地识别开发有针对性的干预措施的关键预测因素.
研究结果支持在解决儿童营养不良问题的公共卫生战略中使用ML.

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