Logistic Regression Model Selection for Unfavorable TB Treatment Outcomes

Wolde Abreham Geda's dataset contains results from logistic regression model selection for predicting unfavorable treatment outcomes among rifampicin/multidrug-resistant tuberculosis patients. The dataset is 5.5 KB in size, stored in an XLS file, and was last updated on April 13, 2026. It is licensed under CC-BY-4.0.

Use Cases

• Training logistic regression models to predict unfavorable treatment outcomes based on clinical predictor variables.
• Comparing model selection techniques for clinical datasets in infectious disease research.
• Analyzing risk factors associated with drug-resistant tuberculosis treatment failure.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• File size of 5.5 KB indicates a focused, manageable scope for analysis.
• Last update date of 2026-04-13 suggests recent maintenance.

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

Wolde Abreham Geda via figshare

Collection Method

Likely contains statistical model selection results from a clinical study.

Time Range

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Freshness

Last updated 2026-04-13 17:31:07; freshness should be verified.

Geography

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