Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer’s Disease

Abstract

Background: In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status. Objective: It remains unexamined how complex patterns of the test performances can be utilized to have specific predictive meaning when the machine learning approach is applied. Methods: In this study, the neuropsychological battery (CERAD-K) and assessment of functioning level (Clinical Dementia Rating scale and Instrumental Activities of Daily Living) were administered to 2,642 older adults with no impairment (n = 285), mild cognitive impairment (n = 1,057), and Alzheimer’s disease (n = 1,300). Predictive accuracy on functional impairment level with the linear models of the single total score or multiple subtest scores (Model 1, 2) and support vector regression with low or high complexity (Model 3, 4) were compared across different sample sizes. Results: The linear models (Model 1, 2) showed superior performance with relatively smaller sample size, while nonlinear models with low and high complexity (Model 3, 4) showed an improved accuracy with a larger dataset. Unlike linear models, the nonlinear models showed a gradual increase in the predictive accuracy with a larger sample size (n > 500), especially when the model training is allowed to exploit complex patterns of the dataset. Conclusion: Our finding suggests that nonlinear models can predict levels of functional impairment with a sufficient dataset. The summary index of the neuropsychological battery can be augmented for specific purposes, especially in estimating the functional status of dementia.

Current Understanding of Verbal Fluency in Alzheimer’s Disease: Evidence to Date

“… 50 The idea that semantic memory and other cognitive functions do not sustain task performance in a regular and constant way during the course of the whole testing minute is reflected by other procedural approaches to CFT scoring. Other than the aforementioned approach based on the characterization of the first 5 retrieved words, 56 , 58 other authors 68 have proposed splitting the global count into four sub-scores, each related to a 15-sec interval. This is to characterize task performance in a way that is more heterogeneous (as there is evidence that the earliest words are more dependent on automatic, rather than controlled semantic memory processing), 59 yet reliant on a scoring procedure that is of easy implementation.…”

Executive Summary of 2023 International Conference of the Korean Dementia Association (IC-KDA 2023): A Report From the Academic Committee of the Korean Dementia Association

“…He presented the findings that linear models demonstrated superior performance with a relatively smaller sample size, whereas nonlinear models with low and high complexity exhibited improved accuracy with a larger dataset. 23 Notably, the nonlinear models showed a gradual increase in predictive accuracy, in particular when the sample size exceeded 500, emphasizing their effectiveness in exploiting complex patterns within the dataset. He suggested that nonlinear models, particularly with sufficient data, can predict levels of functional impairment, offering a valuable augmentation to the summary index of neuropsychological batteries, especially in estimating dementia-related functional status.…”

Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review

“…It comprises a 2 min task to assess attention, a 2 min task to evaluate coordination, and a 4 min task to test memory, with a 30 s break in between. Additionally, research has demonstrated that ML algorithms, using neuropsychological, neurophysiological, and clinical data, have the ability to predict progression to MCI [33][34][35][36]. Notably, educational background has been found to be a factor that contributes to cognitive resilience, thus serving as a protective measure against the development of dementia.…”