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应用机器学习方法来开发孟加拉成年人糖尿病和高血压的预测模型

Gulam Muhammed Al Kibria¹, James Ross O'Hagan², Golam Shariar³

¹Department of international Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.

PLOS global public health

|February 17, 2026

概括

机器学习模型预测孟加拉成年人的高血压和糖尿病,确定超重/肥胖和年龄是关键风险因素. 综合查和生活方式干预对于管理这些非传染性疾病至关重要.

科学领域:

公共卫生公共卫生
生物统计学生物统计学
机器学习应用机器学习应用

背景情况:

高血压和糖尿病等非传染性疾病 (NCD) 是孟加拉国的主要公共卫生问题,城市化和生活方式变化加剧了这种问题.
对非传染性疾病的预测建模对于低收入和中等收入国家的有针对性的干预措施至关重要.

研究的目的:

开发和评估机器学习 (ML) 模型,用于预测孟加拉成年人的高血压和糖尿病.
确定关键预测因素,并了解这些非传染性疾病之间的关系.

主要方法:

利用了2022年孟加拉国人口和健康调查的数据 (n=13,847名成年人).
应用了六个ML模型:ANN,随机森林,AdaBoost,梯度提升,XGBoost和SVM.
确定的高血压 (血压≥140/90 mmHg或药物) 和糖尿病 (FPG≥7.0 mmol/L或药物).

主要成果:

患病率:糖尿病为16.3%,高血压为20.5%.
在模型中观察到高灵敏度但低特异性,特别是对于糖尿病.
AdaBoost显示了糖尿病 (0.699) 和高血压 (0.775) 的最高AUC; SVM实现了糖尿病 (0.836) 的最高准确度.
主要预测因素:高血压预测糖尿病;超重/肥胖,年龄和糖尿病预测高血压. 财富和性有适度的影响.

结论:

机器学习模型可以识别高血压和糖尿病风险的个体,突出其双向关系.
超重/肥胖,年龄较大,财富五分位数较高是重要的预测因素.
建议针对高风险人群进行综合查和生活方式干预,以减少NCD负担.

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