神经药理学报

神经药理学报 ›› 2025, Vol. 15 ›› Issue (2): 28-.DOI: 10.3969/j.issn.2095-1396.2025.02.006

• 研究论文 • 上一篇    下一篇

基于机器学习的阿尔茨海默病健康识别

刘宁，蒋一宁，李轩昂，李兆丰，孙倩   

  1. 浙江科技大学人工智能与信息工程学院，杭州，362000，中国
  • 出版日期:2025-04-25 发布日期:2025-08-25
  • 作者简介:刘宁，博士；研究方向：从事阿尔茨海默病的智能辅助诊断研究；E-mail:625406059@qq.com
  • 基金资助:
    2023年大学生创新创业项目（No.2023cxcy103、No.2023cxcy110），2024年国家级大学生创新创业项目，2024年浙江省教育厅 科研项目（No.Y202454315）

Health Recognition and Linguistic Analysis of Alzheimer's Disease Based on Machine Learning

LIU Ning, JIANG Yi-ning, LI Xuan-ang, LI Zhao-feng, SUN Qian   

  1. Zhejiang University of Science and Technology, Artificial Intelligence and Information Engineering College, Hangzhou, 362000, China
  • Online:2025-04-25 Published:2025-08-25

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摘要：

目的：阿尔茨海默病（Alzheimer's disease，AD）是一种神经退行性病变，在临床上通常需要依靠医生的 工作经验，采用简易神经检测量表更好的方法来诊断AD。方法：本研究中，我们采用Coh-Metrix 语言分析工具 分别对AD 患者和中国健康人群（CN）提取关键的语言学特征，包括语句复杂度、词汇丰富度及语义连续性等， 最后采用支持向量机对文本进行分类。结果：发现在CN 和AD 患者之间某些特定的语言学特征存在显著差异， 这些特征对疾病的识别具有一定的区分能力，最后模型准确率为0.79，取得了较好的结果。结论：基于语言学特 征能够为AD 的健康识别提供有价值的线索和依据，有助于早期诊断和干预。

关键词: 阿尔茨海默病, Coh–Metrix, 健康识别, 文本分类, 机器学习

Abstract:

Objective: Alzheimer disease (AD) is a neurodegenerative disorder. It usually relies on the working experience of doctors and a better method of using the simple neurological test scale for the diagnosis of AD in the Clinic. Methods: In this study, we use the Coh - Metrix language analysis tool to diagnose AD patients from healthy people by extracting the key features of linguistics, including statements complexity, lexical semantics richness and continuity, etc., finally we use support vector machine (SVM) to do the text classification. Results: It was found that there are significant differences in certain specific linguistic features between the healthy population and AD patients. These features have a certain discriminatory ability for disease recognition. Finally, the accuracy rate of the model is 0.79 which achieves a good result. Conclusion: As a result, the study provides valuable clues and evidence based on the linguistic features to identify AD patients which helps to early diagnosis and intervention of AD based on linguistic features.

Key words: Alzheimer's disease, Coh–Metrix, health identification, text classification, machine learning

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