Performance of machine learning algorithms for dementia assessment: impacts of language tasks, recording media, and modalities

Table 1 Lists of abbreviations

Abbreviation	Description
AI	Artificial intelligence
AD	Alzheimer’s disease
ANOVA	Analysis of variance
BI	Brunet’s index
CAMs	Clinical assessment methods
CNN	Convolutional neural network
COVFEFE	COre variable feature extraction feature
DB	Dementia bank
DL	Deep learning
DT	Decision trees
FK	Flesch–Kincaid
FRES	Flesch reading-ease
ET	Extra trees
HS	Honor’s statistic
LDA	Latent Dirichlet allocation
LSPs	Line spectral pairs
kNN	k-Nearest neighbor
Kurt	Kurtosis
MCI	Mild cognitive impairment
MFC	Mel frequency cepstrum
MFCCs	Mel frequency cepstral coefficients
MoCA	Montreal cognitive assessment
ML	Machine learning
mRMR	Minimal redundancy maximal relevance
NLTK	Natural language toolkit
PCA	Principle components analysis
POS	Part-of-speech
PDT	Picture description task
SA	Simple average
SIF	Smooth inverse frequency
SLAMs	Speech and language assessment methods
SRT	Story recall task
SVM	Support vector machine
std	Standard deviation
skew	Skewness
tf-IDF	Term frequency-inverse document frequency
WHO	World Health Organization
VP	Voicing probability

ISSN: 1472-6947