TADPOLE: Alzheimer's Disease Longitudinal Prediction Data

A dataset for Alzheimer's disease prediction containing cognitive, neuroimaging, genetic, and demographic features. It includes three patient classes: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD. The data was used in a study by Emad Al-anbari, last updated on 2026-04-20.

Use Cases

• Predicting Alzheimer's disease progression based on longitudinal cognitive and neuroimaging data.
• Classifying patient status (CN, MCI, AD) using integrated genetic and demographic features.
• Benchmarking novel machine learning models, such as Neural Processes and Normalizing Flows, against traditional approaches for clinical time-series data.

Strengths

• Includes multiple data modalities: cognitive, neuroimaging, genetic, and demographic features.
• License is CC-BY-4.0, allowing for open sharing and reuse.
• Dataset is small (5.5 KB), facilitating quick download and initial exploration.

Limitations

• Row count and specific column definitions are unknown, limiting suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.
• Last updated 2026-04-20 17:47:27; freshness should be verified.

Provenance

Source

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

Collection Method

Features were selected from the TADPOLE dataset for model building.

Time Range

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Freshness

Last updated 2026-04-20 17:47:27.

Geography

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