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机器学习算法用于预测巴布亚新几内亚五岁以下儿童的发育迟缓

Hao Shen¹, Hang Zhao¹, Yi Jiang¹

¹School of Public Health, Chongqing Medical University, Chongqing 400016, China.

Children (Basel, Switzerland)

|October 28, 2023

概括

在巴布亚新几内亚 (PNG) 预防儿童发育迟缓至关重要. 机器学习确定了区域和出生大小等关键预测因素,LASSO-XGBoost显示了早期干预策略的最佳预测性能.

科学领域:

公共卫生公共卫生
儿科儿科儿科
机器学习在健康中的应用

背景情况:

在巴布亚新几内亚 (PNG),儿童发育迟缓仍然是一个持续的挑战,影响长期的健康和发展.
需要有效的预测模型来识别有风险的儿童,以便针对性干预.

研究的目的:

开发和评估机器学习模型,用于预测巴新几内亚五岁以下儿童的发育迟缓.
通过先进的分析技术,识别出缩的最重要的预测因素.

主要方法:

利用了2016-2018年PNG人口健康调查 (n=3380) 的数据.
使用特征选择 (LASSO,RF-RFE) 和预测建模 (逻辑回归,条件决策树,SVM,XGBoost).
评估模型性能使用准确度,精度,回忆,F1得分和AUC,与预测重要性的SHAP值.

主要成果:

拉索-XGBoost模型表现出优异的性能 (AUC:0.765) 对于缩预测.
确定的主要预测因素包括生活在高地地区,孩子的年龄,最富有的家庭财富五分位数和出生大小.
该模型实现了0.728的精度和0.669.1的F1得分.

结论:

机器学习提供了一个强大的工具,用于预测幼儿发育迟缓在PNG.
早期识别高风险因素可以引导有针对性的营养和健康干预措施.
专注于母亲和儿童的营养状况对于预防发育迟缓和改善整体福祉至关重要.

关键词:

巴布亚新几内亚巴布亚新几内亚孩子们的孩子们的孩子们的孩子们.机器学习是机器学习.发育不良发育不良缩发育不良

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