Voice analysis-based machine learning models to diagnose Alzheimer's disease
10.3969/j.issn.1005-202X.2025.05.020
- VernacularTitle:基于语音分析的机器学习模型识别阿尔茨海默病
- Author:
Yuxi ZHANG
1
;
Wei SUN
;
Guodong ZHU
;
Zhiyao REN
;
Ruiqiu ZHANG
Author Information
1. 华南理工大学设计学院,广东 广州 510000;广州市民政科技协同创新中心,广东 广州 510000
- Publication Type:Journal Article
- Keywords:
Alzheimer's disease;
voice analysis;
random forest;
support vector machine;
sequential forward selection
- From:
Chinese Journal of Medical Physics
2025;42(5):685-692
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify key acoustic features associated with the progression of Alzheimer's disease(AD)through voice analysis combined with machine learning and feature selection techniques,thereby constructing classification models that serve as candidate tools for the early screening of AD.Methods Voice samples from AD,mild cognitive impairment(MCI)and healthy(HC)elderly individuals were obtained from the NCMMSC2021 AD voice dataset.The voice samples underwent data preprocessing,followed by feature extraction from the eGeMAPS feature set via the OpenSmile toolkit.Classification models were obtained utilizing random forest and support vector machine(SVM)algorithms.Significance testing and feature importance ranking were conducted using Python,and the further selection of the optimal features was performed through sequential forward selection(SFS).The classification performance before and after feature selection was compared and evaluated using accuracy and the area under the receiver operating characteristic curve(AUC).Results The significant acoustic features in the classification models primarily derived from spectral slope,formant,fundamental frequency,and loudness.The optimal classification performance was achieved with the SVM model following SFS feature selection,with recognition accuracies of 0.926(AUC=0.974)for AD/MCI group,0.875(AUC=0.956)for AD/HC group,and 0.879(AUC=0.904)for MCI/HC group.Conclusion SVM model performs better than random forest model,and the use of SFS for feature selection can effectively enhance model performance.Voice analysis has the potential to serve as a valuable supplementary tool for the rapid AD assessment and screening.
