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Purpose <p>Dementia is a condition with symptoms of memory decline, cognitive impairment, and difficulties in language and problem-solving, among others. Early screening of dementia conditions such as Alzheimer’s disease (AD) is fundamental for quick intervention and disease management. Currently used neuropsychological assessments are time-consuming as they contain many elements and require critical resources which are not always available. Other pathological assessments are invasive and not cost effective, hence identifying cognitive features for different dementia sub-groups during progression of the condition is crucial for clinicians. This study investigates this problem using a cost-effective data driven approach.</p> Methods <p>Using real cases and controls from the Alzheimer’s Disease Neuroimaging Initiative data repository (ADNI) who have undergone the Alzheimer’s Disease Assessment Scale-Cognitive 13 (ADAS-Cog), we conduct a feature-feature assessment together with Permutation Feature Importance (PFI) and machine learning algorithms to derive influential cognitive features for specific dementia groups from baseline diagnosis up to 36 months.</p> Results <p>Feature-feature analysis showed correlations between memory tasks such as Word Recall, Delayed Word Recall, and Word Recognition across both CN-MCI and MCI-AD groups. In contrast, low correlations for Naming, Command, and Ideational Praxis suggest they tap into distinct DSM-5 domains thus making them ideal for early screening. In addition, PFI results showed that Delayed Word Recall emerged as a top cognitive marker of progression in early stages, while Orientation gained prominence later thereby reflecting a shift toward executive and attentional decline.</p> Conclusions <p>The results of this study identified important relationships between cognitive features in the ADAS-Cog and provide a clear example of the value of data-driven machine learning approaches in the identification of markers that indicate disease progression in dementia.</p>

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Cognitive feature evaluation for disease progression in dementia and its precursors using feature selection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月8日

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  • Fadi Thabtah,
  • David Peebles

摘要

Purpose

Dementia is a condition with symptoms of memory decline, cognitive impairment, and difficulties in language and problem-solving, among others. Early screening of dementia conditions such as Alzheimer’s disease (AD) is fundamental for quick intervention and disease management. Currently used neuropsychological assessments are time-consuming as they contain many elements and require critical resources which are not always available. Other pathological assessments are invasive and not cost effective, hence identifying cognitive features for different dementia sub-groups during progression of the condition is crucial for clinicians. This study investigates this problem using a cost-effective data driven approach.

Methods

Using real cases and controls from the Alzheimer’s Disease Neuroimaging Initiative data repository (ADNI) who have undergone the Alzheimer’s Disease Assessment Scale-Cognitive 13 (ADAS-Cog), we conduct a feature-feature assessment together with Permutation Feature Importance (PFI) and machine learning algorithms to derive influential cognitive features for specific dementia groups from baseline diagnosis up to 36 months.

Results

Feature-feature analysis showed correlations between memory tasks such as Word Recall, Delayed Word Recall, and Word Recognition across both CN-MCI and MCI-AD groups. In contrast, low correlations for Naming, Command, and Ideational Praxis suggest they tap into distinct DSM-5 domains thus making them ideal for early screening. In addition, PFI results showed that Delayed Word Recall emerged as a top cognitive marker of progression in early stages, while Orientation gained prominence later thereby reflecting a shift toward executive and attentional decline.

Conclusions

The results of this study identified important relationships between cognitive features in the ADAS-Cog and provide a clear example of the value of data-driven machine learning approaches in the identification of markers that indicate disease progression in dementia.