Multitask Learning Performance for Knee OA, Alzheimer's, and Breast Cancer Diagnosis

5.5 KB of performance metrics for diagnosis tasks across different multitask learning approaches. The dataset, authored by Haresh Rengaraj Rajamohan and last updated in May 2026, compares models using Balanced Accuracy for Knee Osteoarthritis and Macro AUROC, Micro AUROC, and Balanced Accuracy for Alzheimer's Disease and Breast Cancer.

Use Cases

• Benchmarking multitask learning approaches based on reported performance metrics for Knee OA, AD, and BC.
• Comparing the effectiveness of a reference single-task model against multitask models based on the described evaluation metrics.
• Analyzing the trade-offs in model performance across different diseases based on the reported Balanced Accuracy and AUROC scores.

Strengths

• Performance metrics are reported for three distinct medical conditions: Knee Osteoarthritis, Alzheimer's Disease, and Breast Cancer.
• Uses multiple standard evaluation metrics, including Balanced Accuracy and both Macro and Micro AUROC.
• Dataset is licensed under CC-BY-4.0, allowing for open sharing and reuse.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Likely contains results from a research study comparing multitask learning models.

Time Range

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Freshness

Last updated 2026-05-06 17:32:26; freshness should be verified.

Geography

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