机器学习分析了503名住院的2型糖尿病老年患者的回顾性数据,以确定与认知障碍相关的因素
Mingzhu Yu1,2, Jianfeng Zhang1, Haigeng Chen3
1Department of General Practice, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
概括
机器学习准确地预测了2型糖尿病 (T2DM) 的老年人轻度认知障碍 (MCI). 关键因素包括年龄,BMI和糖尿病持续时间,使得早期干预的风险分层更好.
科学领域:
- 老年学是指老年学的学科.
- 神经学 神经学
- 内分泌学 在内分泌学.
背景情况:
- 糖尿病 (DM) 在老年人中越来越令人担忧,轻度认知障碍 (MCI) 作为显著的并发症.
- 目前的MCI预测方法缺乏准确性,需要像机器学习 (ML) 这样的先进方法.
研究的目的:
- 使用ML. 用于识别2型糖尿病 (T2DM) 的老年人中与MCI相关的因素.
- 开发和评估ML模型,以预测该人群中的MCI.
主要方法:
- 对503名60岁以上T2DM住院患者的回顾性分析,分为MCI (n=102) 和正常 (n=401) 组.
- 使用5倍交叉验证,LASSO回归用于特征选择,以及物流回归,XGBoost和随机森林用于预测建模.
- 使用接收器运行特征 (ROC) 曲线比较模型性能.
主要成果:
- 确定了MCI的关键预测因素:年龄,BMI,糖化血红蛋白,C反应蛋白,腰与身高的比例,糖尿病并发症,糖尿病持续时间 (>5年) 和低教育.
- 该XGBoost模型实现了最高的性能:AUC 0.892,准确度 0.851,灵敏度 0.843,特异性 0.859,和F1得分 0.834.
结论:
- XGBoost模型有效地预测T2DM老年患者的MCI,使用已识别的临床因素.
- 这种ML方法可以增强临床风险分层,并支持MCI早期干预策略.
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