TADPOLE: Alzheimer's Disease Prediction Data 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 progression. It includes three diagnostic classes: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD. The dataset, shared by Emad Al-anbari on figshare under CC-BY-4.0, 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 multimodal clinical features.
• Modeling temporal dependencies in patient trajectories for personalized treatment planning.
• Benchmarking novel machine learning models against established methods like SNP or deep geometric learning.

Strengths

• Includes multiple data modalities: cognitive, neuroimaging, genetic, and demographic features.
• Specifically designed for longitudinal prediction of Alzheimer's disease evolution.
• Licensed under CC-BY-4.0, permitting broad reuse with attribution.

Limitations

• Dataset is extremely small at 5.5 KB, indicating very limited scope or a sample.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for model training.

Provenance

Source

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

Collection Method

Selected features for building an efficient prediction model, as described in the associated research.

Time Range

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Freshness

Last updated 2026-04-20 17:47:26; freshness should be verified.

Geography

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