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在老年患者中低骨密度的风险预测,使用监督机器学习算法

Eda Karaismailoğlu¹, Serkan Karaismailoğlu²

¹Department of Medical Informatics, University of Health Sciences Türkiye, Gülhane Faculty of Medicine, Ankara, Türkiye.

Balkan medical journal

|October 31, 2025

概括

机器学习使用患者数据准确预测低骨矿物质密度 (BMD). 确定的主要危险因素包括年龄,性别和血清尿酸,有助于个性化骨质疏松症查.

科学领域:

老年学与公共卫生
生物统计学和机器学习
医疗信息学医疗信息学

背景情况:

低骨矿物质密度 (BMD) 是一个与年龄相关的严重疾病,与骨折风险和死亡率的增加有关.
机器学习 (ML) 通过分析临床,生化和人口统计数据,为早期预测低BMD提供了强大的工具.

研究的目的:

评估11个ML模型的预测能力,以识别低BMD.
使用高性能ML模型精确确定导致低BMD的最重要的风险因素.

主要方法:

一项采用国家健康和营养检查调查 (2005-2020) 数据的横截面研究.
12,108名年龄≥50岁的BMD数据参与者使用57个特征进行了分析.
监督的ML算法被训练并使用准确性和AUC等指标进行评估,并使用SHAP分析来预测其重要性.

主要成果:

额外树木分类器表现出卓越的性能,准确度为76.7%,AUC为0.85.
确定了低BMD的14个关键预测因素,包括性别,年龄,BMI,种族,血清尿酸和糖尿病状况.
通过特征重要性分析,突出了既定的和新的风险因素.

结论:

基于树的ML模型,特别是额外树,对于预测低BMD是有效的.
该研究确定了关键的风险因素,支持针对骨质疏松症和骨质疏松症进行个性化查.
ML有效地捕捉到人口健康数据中的复杂的多因素关系,用于BMD预测.

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