Glial and CSF Biomarkers for Cognitive Impairment Classification in 333 Adults

333 adults aged 60 years and older from the Knight Alzheimer’s Disease Research Center's longitudinal dataset. This dataset was used in a study to evaluate glial and cerebrospinal fluid biomarkers for classifying cognitive impairment, with models achieving AUC up to 0.959. The data was authored by Merve Türkegün Şengül and last updated in April 2026.

Use Cases

• Training classification models to distinguish cognitive impairment based on biomarker levels mentioned in the description.
• Investigating feature importance for clinical dementia rating using SHAP analysis, as referenced in the study results.
• Developing parsimonious diagnostic models using a hybrid feature selection approach described in the methods.
• Comparing the performance of multimodal biomarker models against core CSF-only models for dementia classification.

Strengths

• Includes data from 333 adults, providing a substantive sample for analysis.
• Model performance metrics are reported, with an AUC of 0.959 and accuracy of 0.912 for the top model.
• Licensed under CC-BY-4.0, permitting open sharing and adaptation.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary file is a 638 KB PDF, which is a small document; the underlying tabular data may not be directly accessible.

Provenance

Source

Knight Alzheimer’s Disease Research Center longitudinal, open-access dataset.

Collection Method

Data obtained from a research center's longitudinal study, as stated in the methods.

Time Range

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Freshness

Last updated 2026-04-23 04:20:26; freshness should be verified.

Geography

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