
Gallstone
Donated on 4/19/2025
The clinical dataset was collected from the Internal Medicine Outpatient Clinic of Ankara VM Medical Park Hospital and includes data from 319 individuals (June 2022–June 2023), 161 of whom were diagnosed with gallstone disease. It contains 38 features, including demographic, bioimpedance, and laboratory data, and was ethically approved by the Ankara City Hospital Ethics Committee (E2-23-4632). Demographic variables are age, sex, height, weight, and BMI. Bioimpedance data includes total, extracellular, and intracellular water, muscle and fat mass, protein, visceral fat area, and hepatic fat. Laboratory features are glucose, total cholesterol, HDL, LDL, triglycerides, AST, ALT, ALP, creatinine, GFR, CRP, hemoglobin, and vitamin D. The dataset is complete, with no missing values, and balanced in terms of disease status, eliminating the need for additional preprocessing. It provides a strong foundation for machine learning-based gallstone prediction using non-imaging features.
Dataset Characteristics
Tabular
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Real
# Instances
320
# Features
38
Dataset Information
Has Missing Values?
No
Introductory Paper
By Irfan Esen, Hilal Arslan, Selin Aktürk Esen, Mervenur Gülşen, Nimet Kültekin, Oğuzhan Özdemir. 2024
Published in Medicine
Variables Table
Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
---|---|---|---|---|---|---|
Gallstone Status | Target | Binary | Target variable, Gallstones present(1), and absent(0) | no | ||
Age | Feature | Integer | Age | Age of the person | no | |
Gender | Feature | Categorical | Gender | Gender of the person | no | |
Comorbidity | Feature | Categorical | Concomitant diseases | no | ||
Coronary Artery Disease (CAD) | Feature | Binary | Cardiovascular disease | no | ||
Hypothyroidism | Feature | Binary | Underactive thyroid gland | no | ||
Hyperlipidemia | Feature | Binary | High levels of fat in the blood | no | ||
Diabetes Mellitus (DM) | Feature | Binary | High blood sugar | no | ||
Height | Feature | Integer | Height is the length | no | ||
Weight | Feature | Continuous | Body weight | no |
0 to 10 of 39
Additional Variable Information
Class Labels
- Gallstone Status: 0 (No), 1 (Yes) - Gender: 0 (Male), 1 (Female) - Comorbidity: 0 (No comorbidities present),1 (One comorbid condition), 2 (Two comorbid conditions), 3 (Three or more comorbid conditions) - Coronary Artery Disease: 0 (No), 1 (Yes) - Hypothyroidism: 0 (No), 1 (Yes) - Hyperlipidemia: 0 (No), 1 (Yes) - Diabetes Mellitus: 0 (No), 1 (Yes) - Hepatic Fat Accumulation (HFA): 0 (No fat accumulation),1 (Grade 1 (mild)), 2 (Grade 2 (moderate)), 3 (Grade 3 (severe)), 4 (Grade 4 (very severe))
Dataset Files
File | Size |
---|---|
dataset-uci.xlsx | 76.3 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset gallstone = fetch_ucirepo(id=1150) # data (as pandas dataframes) X = gallstone.data.features y = gallstone.data.targets # metadata print(gallstone.metadata) # variable information print(gallstone.variables)
Esen, I., Arslan, H., Aktürk, S., Gülşen, M., Kültekin, N., & Özdemir, O. (2024). Gallstone [Dataset]. UCI Machine Learning Repository. https://doi.org/10.1097/md.0000000000037258.
Creators
Irfan Esen
Hilal Arslan
hilalarslanceng@gmail.com
Ankara Yildirim Beyazit University, Department of Software Engineering
Selin Aktürk
Mervenur Gülşen
Nimet Kültekin
Oğuzhan Özdemir
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.