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改善2型糖尿病预测:使用AWI-Gen队列平衡数据对机器学习分类器进行比较评估

Richmond Balinia Adda^1,2,3

¹Department of Biometry, School of Mathematical Science, C. K. Tedam University of Technology and Applied Science (UTAS), Navrongo, Ghana.

Research square

|November 24, 2025

概括

机器学习模型可以使用生活方式数据预测非洲的2型糖尿病 (T2DM). 这种方法克服了以前的局限性,为数字健康的早期检测提供了一个强大的工具.

科学领域:

公共卫生公共卫生
机器学习机器学习
流行病学流行病学

背景情况:

2型糖尿病 (T2DM) 是非洲日益严重的健康问题,区域特定的预测模型有限.
现有的研究面临着数据泄露,类不平衡和过度配合等挑战,阻碍了临床使用,特别是在数字健康领域.

研究的目的:

开发和验证针对非洲人群量身定制的T2DM机器学习 (ML) 预测模型.
解决先前研究中的方法限制,并使其能够在数字健康环境中部署.

主要方法:

分析了加纳H3Africa AWI-Gen队列的2,010名参与者的数据.
应用严格的预处理,包括用于类不平衡的SMOTE和排除易泄漏的生物标记物的SMOTE.
用贝叶斯超参数优化和5倍交叉验证对8个ML分类器进行评估.

主要成果:

优化的XGBoost模型实现了0.845.5的AUC.
排除葡萄糖作为预测因素至关重要,以避免有偏见的评估.
使用人体测量和生活方式数据 (AUC = 0.783) 的模型显示出强大的预测能力,腰围,体力活动和BMI是关键预测因素.

结论:

使用常规临床和生活方式数据的ML模型可以在非洲数字健康应用中实现临床相关的T2DM预测.
这项研究为早期T2DM检测提供了一个强大的,数据驱动的框架,解决了先前的方法差距.
这些发现对公共卫生政策和类似环境中的数字查计划有影响.

关键词:

非洲非洲非洲临床验证临床验证数字健康数字健康机器学习机器学习预测建模预测建模 2 型糖尿病 2 型糖尿病在XGBoost上使用.

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