探索机器学习算法来预测腹疾病,并确定其在东非五岁以下儿童中的决定因素
Tirualem Zeleke Yehuala1, Nebiyu Mekonnen Derseh2, Makda Fekadie Tewelgne3
1Department Health informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. sarazeleke3@gmail.com.
Journal of epidemiology and global health
|July 29, 2024
概括
腹是导致儿童死亡的主要原因. 机器学习模型可以预测腹疾病并确定关键决定因素,有助于东非儿童生存策略.
科学领域:
- 公共卫生 公共卫生
- 机器学习应用 机器学习应用
- 儿科疾病预测预测
背景情况:
- 在全球范围内,腹是导致五岁以下儿童死亡的第二大原因.
- 早期预测和识别腹决定因素对于儿童的生存至关重要.
- 机器学习为预测儿童疾病提供了先进的工具.
研究的目的:
- 用机器学习预测五岁以下儿童的腹疾病.
- 确定导致儿童腹的关键决定因素.
- 为疾病预防和管理制定可行的规则.
主要方法:
- 利用了来自12个东非国家的二次数据 (DHS数据集).
- 采用机器学习技术,包括随机森林,决策树,K-最近邻居和物流回归.
- 应用包装的特征选择和SHAP值,以确定重要的决定因素.
主要成果:
- 随机森林模型在预测腹方面取得了最高的准确性 (86.5%).
- 确定的主要预测因素包括年龄,国家,财富,母亲教育,水源,免疫和养做法.
- 一些因素,如母亲的教育和获得卫生设施,与腹的可能性降低有关.
结论:
- 儿童护理人员对卫生和养实践的认识至关重要.
- 母亲教育是减少儿童因腹死亡率的一个重要因素.
- 专注于教育和卫生的政策干预可以降低东非的婴儿死亡率.
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