Cognitive impairment recognition based on random forest model with acoustic feature
TAO Liangwei
LI Yiming
CHEN Quan
ZHOU Zhixing
JI Zhiyu
FU Hang
ZHANG Yanjie
YANG Hui
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.
Keywords：Machine learningAlzheimer's diseaseMild cognitive impairmentSpeechNatural language processing
Publication Date：2026-06-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 204-211 )