Relationship between language features extracted through NLP and clinically diagnosed Alzheimer's disease and mild cognitive impairment in Slovak

Abstract

BACKGROUND: Dementia, particularly Alzheimer's disease (AD), affects language, especially lexical-semantic processing. Discourse analysis using NLP methods can aid early detection, but research in inflectional languages like Slovak is limited. METHODS: Speech samples from 216 Slovak-speaking participants (64 AD, 44 MCI, 108 HC) were collected using a picture description task and analyzed for lexical complexity using 15 NLP-based measures. RESULTS: Several lexical complexity measures, including GTTR, UBER, SICHEL, MTLD, HDD and others, significantly differentiated AD or MCI from healthy controls. Some measures (UBER, YULEI, HONORE) also distinguished between AD and MCI. CONCLUSION: Lexical complexity metrics can serve as non-invasive linguistic indicators of neurodegenerative diseases, demonstrating diagnostic relevance for early detection of AD and MCI in Slovak. Highlights: Lexical complexity metrics effectively differentiate between healthy controls, MCI, and AD in Slovak speakers. Measures such as GTTR, UBER, and HONOR exhibit strong diagnostic potential for neurodegenerative diseases. Education significantly influences linguistic deficits, with higher education correlating to reduced cognitive decline. Findings underscore the importance of studying minority languages for advancing AD and MCI diagnostics.