开发一种基于机器学习和雾计算的可解释的视觉评级表,用于预测痴呆症进展
Zainab H Ali1,2, Esraa Hassan3, Shimaa Elgamal4
1Department of Embedded Network Systems and Technology, Faculty of Artificial Intelligence, Kafrelsheikh University, El-Geish St, Kafrelsheikh, 33516, Egypt. zainabhassan@ai.kfs.edu.eg.
Scientific reports
|July 16, 2025
概括
这项研究引入了一种新的雾计算系统,用于使用心理测试来早期检测痴呆症. 该系统利用AdaBoost模型实现了93%的准确性,超过了传统的认知评估.
科学领域:
- 老年学和认知科学 认知学和认知科学
- 医疗信息学和机器学习
背景情况:
- 目前的痴呆症研究通常依赖于机器学习模型的磁共振成像 (MRI),但这些方法可能无法检测早期受影响的大脑区域.
- 像迷你精神状态检查 (MMSE) 和蒙特利尔认知评估 (MoCA) 这样的认知评估为痴呆的可能性和认知障碍提供了有价值的指标.
研究的目的:
- 开发和评估一个创新的,可靠的,环境意识的健康监测系统,使用雾计算实时测量老年人的精神障碍.
- 在雾计算框架内利用MMSE和MoCA选测试,用于早期检测和及时治疗认知障碍.
主要方法:
- 采用了450名受试者的数据集 (150人患有轻度认知障碍 (MCI),150人患有帕金森病 (PD),150人患有阿尔茨海默病 (AD)).
- 用一个集成的AdaBoost机器学习 (ML) 模型来准确地确定和分类健康风险.
- 用拟议的分数测试,对另外18名受试者 (每类6人) 验证了ML模型的有效性.
主要成果:
- 拟议的ML模型在诊断认知障碍方面实现了0.93的高精度.
- 该系统的性能超过了传统的分数:MoCA (0.90) 和MMSE (0.83).
- 该AdaBoost模型在准确性,精度,回忆,F-score和曲线下的面积 (AUC) 中表现出卓越的性能.
结论:
- 情境感知雾计算方法通过有效利用心理测试成绩,显著提高早期痴呆症诊断.
- 该系统为实时认知功能监测和老年人早期干预提供了一个有希望的,可靠的方法.
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