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机器学习算法和用于检测痴呆症的传统统计模型:基于人口的研究

Yuqi Li¹, Tingting Hou¹, Jiaqi Dong¹

¹Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.

|December 11, 2025

概括

机器学习模型,包括XGBoost,在检测中国老年人的痴呆症方面表现出很高的准确性. 这些模型为农村医疗保健环境中早期痴呆症诊断提供了一种实用方法.

科学领域:

老年学是一门学科.
医疗信息学医疗信息学
流行病学流行病学

背景情况:

早期发现痴呆症对于及时干预来减缓疾病进展至关重要.
这项研究侧重于在中国农村老年人群中诊断痴呆症.
评估机器学习和逻辑回归用于痴呆症诊断.

研究的目的:

评估机器学习算法 (随机森林,XGBoost) 和用于痴呆症检测的后勤回归 (LR) 的性能.
为了比较这些模型在中国农村老年人群中的诊断准确性和临床实用性.
在资源有限的环境中确定早期痴呆症查的实用工具.

主要方法:

一项基于人口的研究,涉及5200名参与者.
最小绝对收缩和选择运算符 (LASSO) 回归用于LR中的预测器选择.
随机森林 (RF) 和极端梯度增强 (XGBoost) 模型的实施.
模型性能评估使用接收器操作特征曲线 (AUC) 下的面积,校准图和决策曲线分析 (DCA).

主要成果:

在验证组中,XGBoost模型实现了0.95 (95%CI:0.94-0.98) 的最高受体运行特征曲线 (AUC) 下的面积.
后勤回归 (LR) 和随机森林 (RF) 模型显示AUC为0.88.
与没有干预或通用干预策略相比,所有模型都表现出良好的校准和卓越的临床实用性.

结论:

机器学习模型,特别是XGBoost,在中国农村老年人群中检测痴呆症方面表现出色.
这些模型为痴呆症检测提供了高精度和临床实用性.
这些模型利用了随时可用的临床数据,为农村老年人群的日常实践提供了易于实施的方法.

关键词:

曲线下的面积 (AUC)校准图表中的一个是校准图表.决策曲线分析 (DCA) 的方法痴呆症痴呆症是一种痴呆症.诊断模型诊断模型诊断模型机器学习 (ML) 是指机器学习.老年人年长的人.

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