Knowledge-Based Model for Detecting Neurodegenerative Diseases Using Text Complexity Measures

Abstract

The incidence of neurodegenerative diseases affecting the brain and its cognitive functions, including language and speech, is increasing in society. These diseases impact the manner and quality of speech and can be detected through non-invasive methods. Understanding language involves analyzing internal linguistic features such as text readability and complexity. Language complexity is a significant measure of an individual's linguistic development, representing an independent dimension of utterance (whether written or spoken) and manifesting across all linguistic levels (phonological, morphological, syntactic, and semantic). The aim of this research is to identify linguistic features - measures of text complexity - that may serve as predictors for a knowledge-based model to detect neurodegenerative diseases such as Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Parkinson's Disease (PD) in the context of the inflectional Slovak language. The results indicate that lexical measures of language complexity that are independent of text length are unsuitable for predicting neurodegenerative diseases such as AD/MCI or PD. However, they can be useful in distinguishing between AD/MCI and PD. The rate of action in describing a situational picture is a strong predictor for distinguishing AD/MCI but not PD. The sequence of two verbs is a strong predictor for diagnosing both AD/MCI and PD, but does not distinguish between these diseases. Last but not least, vocabulary range and diversity influence not only the diagnosis of neurodegenerative diseases, but also help differentiate between AD and PD.