使用机器学习模型进行阿尔茨海默氏症疾病分类的增强手写动态建模.
Rohith R1, Sakthi Jaya Sundar Rajasekar2, Thangavel Murugan3
1Department of Information Science and Technology, College of Engineering, Guindy Campus, Anna University, Chennai, India.
Studies in health technology and informatics
|July 1, 2025
概括
使用机器学习 (ML) 的手写分析显示,在检测阿尔茨海默氏症 (AD) 方面具有很高的准确性. 这种非侵入性方法为早期查和AD的预后评估提供了有希望的方法.
科学领域:
- 神经学 神经学
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,影响认知和运动功能.
- 手写障碍是阿尔茨海默病的一个公认症状,影响精细运动控制和认知处理.
研究的目的:
- 使用机器学习 (ML) 调查手写分析用于阿尔茨海默病检测的有效性.
- 评估各种ML模型在基于手写特征的AD分类中的性能.
主要方法:
- 收集并预处理了一组手写样本的数据集.
- 使用规范化和合成少数人过量采样技术 (SMOTE) 实现了数据平衡.
- 训练和评估了包括多层感知器 (MLP) 在内的多个机器学习模型.
主要成果:
- 多层感知器 (MLP) 模型实现了99.26%的分类精度.
- 该研究证明了ML在通过手写分析识别AD方面的潜力.
结论:
- 用ML增强的手写分析,为AD查提供了一个高度准确和非侵入性的方法.
- 这种方法可以帮助早期发现和预后评估阿尔茨海默病.
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