在基于机器学习的2型糖尿病患者中预测糖尿病脚
Haixiang Zhang1, Weijian Fan1, Peipei Li2
1The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, 230012, China.
Biomedical engineering online
|January 16, 2026
概括
机器学习模型通过选择年龄和葡萄糖水平等关键特征,准确地预测糖尿病脚 (DF) 风险. 基于信息理论的Kernel极端学习机器 (IT-KELM) 在早期DF查方面表现出强大的诊断性能.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的机器学习
- 糖尿病并发症预测预测
背景情况:
- 糖尿病脚 (DF) 是2型糖尿病 (T2DM) 的严重并发症,导致严重的发病率和医疗费用.
- 早期预测和干预对于预防截肢和改善患者结果至关重要.
- 传统的统计方法在DF风险中进行最佳特征识别时,与高维数据作斗争.
研究的目的:
- 开发和验证用于糖尿病足风险预测的机器学习模型.
- 利用基于二进制物流回归和信息理论的特征选择策略.
- 确定T2DM患者早期DF检测的最佳预测特征.
主要方法:
- 追溯分析1,179名患者 (95例DF病例,1084例T2DM对照) 的临床和生化数据.
- 三个数据集的构建:原始特征,通过后勤回归 (F1) 选择的特征和信息理论特征 (F2).
- 使用五倍交叉验证对六种模型 (ELM,KELM变体) 的评估,评估AUC,灵敏度,特异性和计算效率.
主要成果:
- 年龄,BUN,Hcy,ALB和FBG被确定为独立的DF风险因素.
- 基于信息理论的KELM (IT-KELM) 模型在F2数据集中实现了最高的AUC (0.799).
- 特性选择提高了准确性和减少了计算时间;IT-KELM在0.138s中训练并在0.0023s中测试.
结论:
- 将信息理论与KELM集成,可以通过优化的特征子集和非线性映射来增强DF风险预测.
- 该IT-KELM模型展示了强大的诊断性能和早期DF查的临床可行性.
- 这种方法为糖尿病护理中的精准医学提供了一个具有成本效益的工具,需要多中心验证.
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