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多模式机器学习用于预测使用上坡路驾驶的粉样蛋白阳性.

Sai Santosh Reddy Danda¹, Yi Lu Murphey¹, Amanda Maher^2,3

¹Department of Electrical Electronics and Communication Engineering University of Michigan-Dearborn Dearborn Michigan USA.

Alzheimer's & dementia (Amsterdam, Netherlands)

|August 13, 2025

概括

机器学习可以准确地检测粉样蛋白阳性,这是阿尔茨海默病的早期指标,使用驾驶行为和生理数据. 这种非侵入性方法对老年人早期认知衰退的检测有前途.

关键词:

粉样蛋白的水平是多少驾驶行为驾驶行为早期认知评估早期认知评估高速公路合并高速公路合并在上坡路上驾驶.生理反应的生理反应

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科学领域:

神经学神经学
数据科学数据科学数据科学
老年学是一门学科.

背景情况:

早期发现粉样蛋白阳性对于识别阿尔茨海默病 (AD) 风险至关重要.
本研究探讨了用于认知健康监测的非侵入性方法.

研究的目的:

使用多模式数据对具有和没有粉样蛋白阳性的老年人进行分类.
评估机器学习模型在预测粉样蛋白状况方面的有效性.

主要方法:

收集了53名认知正常的老年司机的驾驶和生理数据.
使用随机森林和XGBoost分类器,对统计学上显著的特征进行训练 (P ≤0.05).
分析了多模式属性,包括车辆,生理和人口统计数据.

主要成果:

整合多种数据模式改善了粉样蛋白状况的分类性能.
使用所有显著特征,XGBoost模型实现了最高准确度 (85.1%).
车辆数据,特别是上坡道驾驶行为,显示出最具预测能力.

结论:

在上坡路上进行多模式数据分析,用于早期认知衰退检测的驾驶辅助工具.
具有挑战性的交通环境可以用于非侵入性认知健康监测.
结果突显了在预测粉样蛋白状况和早期AD检测方面,上坡道驾驶洞察的重要性.