基于机器学习模型和可解释算法的外周血管疾病和糖尿病糖尿病的糖尿病足的预测模型和风险因素识别
Yifeng Pan1, Guangrong Tao2, Bing Chen1
1Department of Vascular Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China.
Medicine
|October 7, 2025
概括
这项研究开发了一种机器学习模型,用于预测糖尿病外周血管疾病和糖尿病脚并发症. 该GA-BPNN模型有效地使用纤维素和C-反应蛋白等关键指标识别了高风险患者.
科学领域:
- 医疗信息学 医疗信息学
- 心血管医学 心血管医学
- 内分泌学 在内分泌学.
背景情况:
- 糖尿病外周血管疾病 (DPVD) 和糖尿病足 (DF) 显著损害糖尿病患者的生活质量.
- 早期识别患有DPVD和DF风险的患者对于有效管理和预防残疾至关重要.
研究的目的:
- 开发和验证用于预测2型糖尿病患者DPVD和DF的机器学习模型.
- 确定与这些糖尿病并发症相关的关键指标和危险因素.
主要方法:
- 分析了1240名2型糖尿病患者的截面数据.
- 机器学习算法包括随机森林,SVM,BPNN,XGBoost和SHAP用于特征重要性排名.
- 为了预测,开发了一个基因算法优化的逆向传播神经网络 (GA-BPNN),使用重和SHAP分析进行特征选择.
主要成果:
- 该GA-BPNN模型表现出强大的预测性能,糖尿病和DPVD (G1) 的AUC为0.79,DPVD和DF (G2) 的AUC为0.89.
- 准确率为0.78 (G1) 和0.80 (G2),其中F1分数为0.77 (G1) 和0.83 (G2).
- 确定的关键指标包括纤维素和C反应蛋白,由SHAP分析证实.
结论:
- 该GA-BPNN模型是有效的预测DPVD和DF在糖尿病患者.
- 该研究成功地确定了关键的风险因素,使得风险分层和有针对性的干预措施更好.
- 这种方法整合了各种指标,以全面预测糖尿病并发症.
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