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基于人工智能的糖尿病病诊断,使用尿液VOC生物传感器数据

Chatchai Kreepala¹, Watcharapong Anakkamatee², Anawin Pechbooranin³

¹Nephrology Unit, School of Internal Medicine, Institute of Medicine, Suranaree University of Technology, 111 University Avenue, Suranaree Subdistrict, Mueang Nakhon Ratchasima District, Nakhon Ratchasima, 30000, Thailand. chatchaikree@gmail.com.

|November 27, 2025

概括

这项研究表明,通过机器学习分析尿液挥发性有机化合物 (VOC) 可以帮助非侵入性诊断糖尿病病 (DKD). 随机森林模型在区分DKD与其他脏疾病方面表现出高准确性.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
生物标志物发现发现
人工智能在医学中的应用

背景情况:

糖尿病病 (DKD) 是全球慢性病的主要原因.
目前对DKD的诊断方法是侵入性 (脏活检) 或依赖间接生物标志物.
需要对DKD进行非侵入性诊断工具.

研究的目的:

评估使用尿液挥发性有机化合物 (VOC) 分析进行非侵入性DKD分类的可行性.
评估机器学习模型在从VOC配置文件中识别DKD方面的性能.
探索VOC分析作为诊断选工具的潜力.

主要方法:

收集了来自四个组的127名参与者的尿样:健康对照组,没有病的糖尿病,活检确认的DKD和原发性综合征.
分析样品使用化学阻断型VOC生物传感器,提取超过15,000个信号衍生特征.
训练并验证了四个机器学习分类器 (Random Forest,SVM,k-NN,Naïve Bayes) 使用SMOTE进行类平衡和分层数据.

主要成果:

随机森林模型实现了最高的性能:准确率为86%,精度为0.91,回忆率为0.86,F1得分为0.86,AUC为0.95.
K-fold 交叉验证证实了该模型的稳定性和通用性.
随机森林有效地区分了DKD与其他糖尿病和非糖尿病球体疾病.

结论:

尿液VOC分析与机器学习相结合,为非侵入性DKD诊断提供了概念验证.
随机森林模型显示出作为鉴定工具的承诺,用于识别DKD.
这种方法可以减少活检的需要,并促进在临床实践中更早地检测DKD.

关键词:

人工智能的人工智能是人工智能.生物传感器是一种生物传感器.糖尿病脏疾病糖尿病脏疾病尿液分析尿液分析挥发性有机化合物挥发性有机化合物

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