生物医学工程研究2026，Vol.45Issue(3)：204-211,8.DOI:10.19529/j.cnki.1672-6278.2026.03.04
基于声学特征的随机森林模型的认知障碍识别
Cognitive impairment recognition based on random forest model with acoustic feature
摘要
Abstract
Aiming at the problems of cumbersome process and high cost of existing early screening methods for Alzheimer's disease(AD),we proposed a cognitive impairment recognition model based on machine learning.Firstly,we extracted the acoustic and seman-tic features of speech signals,combined with the Shanghai cognitive screening(SCS)scale test scores and personal information to im-prove the accuracy of identifying cognitive impairments.Then,Shapley additive explanations(SHAP)and local interpretable model-ag-nostic explanations(LIME)were used to analyze the importance of different speech features to enhance the interpretability of the mod-el.The results of manual Q&A dataset showed that the recognition accuracy,area under the receiver operating characteristic curve(AUC)and F1 score for cognitive impairments reached 0.9000,0.9023 and 0.9000,respectively.The study can provide new insight for early detection of AD.关键词
机器学习/阿尔茨海默病/轻度认知障碍/语音/自然语言处理Key words
Machine learning/Alzheimer's disease/Mild cognitive impairment/Speech/Natural language processing分类
医药卫生引用本文复制引用
陶良伟,李一鸣,陈泉,周知行,季志煜,付航,张言杰,杨晖..基于声学特征的随机森林模型的认知障碍识别[J].生物医学工程研究,2026,45(3):204-211,8.基金项目
上海市教委AI赋能科研计划重点项目(SHJWAIJK241201). （SHJWAIJK241201）
