Using a nonparametric item response theory model to identify patterns of cognitive decline: The Mokken scale analysis.

Cognitive decline, particularly in dementia, presents complex challenges in early detection and diagnosis. While Item Response Theory (IRT) has been instrumental in identifying patterns of cognitive impairment through psychometric tests, its parametric models often require large sample sizes and strict assumptions. This creates a need for more adaptable, less demanding analytical methods. This study aimed to evaluate the effectiveness of Mokken scale analysis (MSA), a nonparametric IRT model, in identifying hierarchical patterns of cognitive impairment from psychometric tests. Using data from 1164 adults over 60 years old, we applied MSA to the orientation subscale of ACE-III. Our analysis involved calculating scalability, monotone homogeneity, invariant item ordering (IIO) and response functions. The MSA effectively retrieved the hierarchical order of cognitive impairment patterns. Most items showed strong scalability and consistent patterns of cognitive performance. However, challenges with IIO were observed, particularly with items having adjacent difficulty parameters. The findings highlight MSA's potential as a practical alternative to parametric IRT models in cognitive impairment research. Its ability to provide valuable insights into patterns of cognitive deterioration, coupled with less stringent requirements, makes it a useful tool for clinicians and researchers.

Application of machine learning techniques for dementia severity prediction from psychometric tests in the elderly population.

Previous research has shown the benefits of early detection and treatment of dementia. This detection is usually performed manually by one or more clinicians based on reports and psychometric testing. Machine learning algorithms provide an alternative method of prediction that may contribute, with an automated process and insights, to the diagnosis and classification of the severity level of dementia. The aim of this study is to explore the use of neuropsychological data from a reduced version of the Addenbrooke's Cognitive Examination III (ACE-III) to predict absence or different levels of dementia severity using the Global Deterioration Scale (GDS) scores through the implementation of the kNN machine learning algorithm. A sample of 1164 elderly people over sixty years old were evaluated using a reduced version of the ACE-III and the GDS. The kNN classifier provided good accuracies using 15 items from the ACE-III and adequately differentiating people with absence and mild impairment, from those with more severe levels of impairment according to the GDS rating. Our results suggest that the kNN algorithm may be used to automate aspects of clinical cognitive impairment classification in the elderly population.