TADPOLE: Alzheimer's Disease Prediction Dataset with Cognitive and Neuroimaging Features

The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) dataset contains cognitive, neuroimaging, genetic, and demographic data for predicting Alzheimer's disease progression. It includes three diagnostic classes: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD. The dataset, shared by Emad Al-anbari on figshare, is a 5.5 KB XLS file last updated in April 2026.

Use Cases

• Predicting Alzheimer's disease progression based on longitudinal cognitive and neuroimaging data.
• Classifying patients into CN, MCI, or AD categories using demographic and genetic features.
• Modeling temporal dependencies in patient trajectories for early detection.
• Benchmarking new predictive models against established approaches like SNP and deep geometric learning.

Strengths

• Includes multiple data modalities mentioned in the description: cognitive, neuroimaging, genetic, and demographic.
• Dataset is licensed under CC-BY-4.0, permitting open sharing and adaptation.
• Specifically designed for longitudinal evolution prediction, capturing temporal dependencies.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating a limited scope, likely a subset or processed features.

Provenance

Source

TADPOLE (The Alzheimer's Disease Prediction of Longitudinal Evolution) dataset.

Collection Method

Features were selected from the TADPOLE dataset for model building, as stated in the description.

Time Range

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Freshness

Last updated 2026-04-20 17:47:24; freshness should be verified.

Geography

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