在低收入和中等收入国家使用机器学习模型预测高血压
Ekaba Bisong1, Noor Jibril2, Preethi Premnath3
1SiliconBlast Ltd., Calgary, AB, Canada. ebisong@siliconblast.com.
BMC medical informatics and decision making
|August 23, 2024
概括
这项研究开发了可解释的机器学习模型来预测高血压,这是一个主要的非传染性疾病风险因素. 模型显示不同国家的准确性有所变化,突出显示了全球卫生监测中需要量身定制的方法.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗保健中的机器学习
背景情况:
- 非传染性疾病 (NCD) 是一个日益严重的全球健康问题,高血压是关键的危险因素.
- 高血压的有效管理对于全球控制非传染性疾病至关重要.
研究的目的:
- 开发和评估可解释的机器学习模型,利用全球调查数据预测高血压.
- 在不同的人口和地理区域中确定高血压的关键预测因素.
主要方法:
- 利用了来自57个国家参与NCD风险因素监测 (STEPS) 调查的STEPwise方法的统一数据.
- 训练并评估了五个机器学习模型 (逻辑回归,k-NN,随机森林,XGBoost,神经网络) 使用80/20的训练测试分割.
- 评估模型性能,使用准确度,精度,回忆和F1得分,并进行特征重要性分析.
主要成果:
- 高血压的主要预测因素包括年龄,体重,心率,腰围和身高.
- 模型准确性在不同国家有很大差异,从58.96%到81.41%不等.
- 绩效差异凸显了区域和国家特定建模策略的必要性.
结论:
- 可解释的机器学习模型可以帮助人口层面的查和高血压风险评估.
- 这些发现强调了调整预测模型以适应当地环境的重要性,以有效监测NCD风险因素.
- 这种方法为资源有限的环境提供了一种有价值的工具,以管理高血压和减轻非传染性疾病.
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