Examining Cognitive Factors for Alzheimer’s Disease Progression Using Computational Intelligence

Abstract

Prognosis of Alzheimer’s disease (AD) progression has been recognized as a challenging problem due to the massive numbers of cognitive, and pathological features recorded for patients and controls. While there have been many studies investigated the diagnosis of dementia using pathological characteristics, predicting the advancement of the disease using cognitive elements has not been heavily studied particularly using technologies like artificial intelligence and machine learning. This research aims at evaluating items of the Alzheimer’s Disease Assessment Scale-Cognitive 13 (ADAS-Cog-13) test to determine key cognitive items that influence the progression of AD. A methodology that consists of machine learning and feature selection (FS) techniques was designed, implemented, and then tested against real data observations (cases and controls) of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository with a narrow scope on cognitive items of the ADAS-Cog-13 test. Results obtained by ten-fold cross validation and using dissimilar classification and FS techniques revealed that the decision tree algorithm produced classification models with the best performing results from the cognitive items. For ADAS-Cog-13 test, memory and learning features including word recall, delayed word recall and word recognition were the key items pinpointing to AD advancement. When these three cognitive items are processed excluding demographics by C4.5 algorithm the models derived showed 82.90% accuracy, 87.60% sensitivity and 78.20% specificity.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review

“…Based on this comparison, one’s most recent cognitive functioning can be evaluated ( Grossman et al, 1996 ; Hansen et al, 2018 ; Dunne et al, 2021 ). Nonetheless, despite being considered cost-effective and straightforward to administer, cognitive function tests are not sufficiently sensitive to identify the progression of MCI (for example, ADAS-Cog may be less responsive to change when used in people with MCI; Skinner et al, 2012 ; Thabtah et al, 2022b ). Notably, as writing and drawing are required in some tests, motor impairments such as post-stroke paralysis (frequently observed in patients with dementia) can lead to lower scores and inaccurate diagnoses ( Palsetia et al, 2018 ; Heyrani et al, 2022 ).…”

Assessing Cognitive Features of Dementia Progression for Different Dementia Levels using Feature Selection

“…The results were promising in which processing only three cognitive elements by the classi cation algorithms, particularly C4.5, revealed high predictive classi cation models. Overall, the results of Thabtah et al (2022a;b) showed that cognitive elements are more impactful in regards to dementia progression than functional activities, at least with the data and methods considered. However, the studies by Thabtah et al 2018) determined that the ability of ADAS-Cog-13 to identify disease progression was better than that of ADAS-Cog-11, subsequently pinpointing the various versions of ADAS-Cog assessment available making it di cult for cross-comparison of validity and reliability.…”

Assessment for Alzheimer’s Disease Advancement Using Classification Models with Rules

“…While pathological assessments of Alzheimer's disease (AD) diagnosis, such as using biological markers (biomarkers), and magnetic resonance imaging (MRI) [5] can be used to predict the disease, they are also time-and cost-intensive, stressful, and provide results requiring laboratory study and professional personnel who may not be available [6][7][8]. Therefore, cognitive assessments used for prodromal dementia such as the Alzheimer's Disease Assessment Scale-Cognitive 13 (ADAS-Cog-13), Mini Mental State Examination (MMSE), and others [9,10] are useful since they can screen for signs of early impairment.…”

“…However, identifying a few cognitive items that can be signs of the progression of the disease and can assist in early intervention is still a challenge [12]. Additionally, very few research studies have measured the progression of dementia using cognitive features with a data drive methodology, i.e., [6,8,[13][14][15].…”

“…While Battista et al [8] used machine learning to assess cognitive measures, their study focused on diagnosing AD and not the progression of the disease, which is more challenging, and it did not consider the criteria defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) framework [16]. Moreover, Thabtah et al [6] investigated functional elements that may have an influence on dementia advancement using real data from the ADNI data project, and no cognitive elements were considered in the evaluation. In addition, AlShboul et al [15] studied real data related to the Clinical Dementia Rating-Sum of Boxes (CDR-SB) assessment method in the ADNI project to reveal dementia indicators and understand their impact on dementia diagnosis.…”