Gallstone Disease Prediction with 38 Clinical Features and Model Performance

A publicly available tabular dataset for predicting the presence of gallstones, a condition affecting the gallbladder. The dataset likely contains 38 clinical features, including C-Reactive Protein (CRP) and Vitamin D, which were identified as the most influential for prediction. It was authored by Prosenjit Das and last updated on June 1, 2026.

Use Cases

• Benchmarking classification algorithms like CatBoost based on performance metrics such as F1-score and accuracy.
• Studying feature selection techniques using methods like the Sea Lion Optimization Algorithm (SLOA).
• Applying model explainability frameworks such as SHAP, LIME, and DiCE to clinical prediction tasks.
• Investigating the predictive relationship between biomarkers like C-Reactive Protein (CRP) and Vitamin D with gallstone disease.

Strengths

• Includes specific model performance metrics, such as a mean F1-score of 79.01% for the CatBoost model.
• Model explainability results identify C-Reactive Protein (CRP) and Vitamin D as key predictive features.
• Released under a permissive CC-BY-4.0 license for open use and sharing.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale modeling.
• 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 or sample size.

Provenance

Source

figshare

Collection Method

Method of data collection is not specified in the provided description.

Freshness

Last updated 2026-06-01 17:51:40; freshness should be verified.