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基于常规血液和生化检测数据,构建可解释的机器学习勃起功能障碍诊断模型

Yanghao Tai^1,2, Bin Chen¹, Yingming Kong¹

¹Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences Tongji Shanxi Hospital, Taiyuan, 030032, China.

European journal of medical research

|February 26, 2026

概括

机器学习模型分析了常规血液标记物,以确定勃起功能障碍 (ED) 的潜在风险因素. 葡萄糖血红蛋白,乳酸脱酶和红细胞分布宽度的升高与ED易感性增加有关.

科学领域:

泌尿器科泌尿器科泌尿器科泌尿器科
生物化学生物化学
数据科学数据科学数据科学

背景情况:

勃起功能障碍 (ED) 的发病率很高,但由于目前的限制和患者的不情愿,诊断是具有挑战性的.
机器学习 (ML) 在疾病诊断方面表现有前途,但尚未使用标准血液标记物应用于ED.

研究的目的:

通过机器学习探索常规血液和生化标志物用于勃起功能障碍的诊断潜力.
为了确定特定的血液参数与增加易受性障碍的相关性.

主要方法:

利用了来自国家健康和营养检查调查 (2001-2004) 的数据.
采用特征选择 (博鲁塔算法) 和逐步回归.
开发和评估了多种ML模型,包括后勤回归,XGBoost,SVM,LightGBM和CatBoost,并通过SHAP分析进行解释.

主要成果:

包括945名ED患者和2520名没有ED患者;在常规血液参数中观察到显著差异.
常规血液和生化标志物,包括高血糖血红蛋白,乳酸脱酶和红细胞分布宽度,与ED风险增加相关.
CatBoost模型在特定的标准化净收益门内证明了效用.

结论:

常规的血液和生化参数显示出了解勃起功能障碍的病因学的潜力.
ML模型可以有效地分析这些标记,以评估ED风险.
对这些标记物的进一步研究可以改善ED诊断和管理.

关键词:

人工智能的人工智能是人工智能.在 CatBoost 中使用 CatBoost.诊断模型是一个诊断模型.勃起功能障碍是因为勃起功能障碍.机器学习是机器学习.尼汉斯 (NHANES) 是一个名人.预测模型的预测模型.这就是 SHAP SHAP 的意思.

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