UC Irvine
ML Repository
Theme

Gallstone

Download(73.4 KB)
Thumbnail

About

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.
Subject Area
Computer Science
Instances
320
Features
37
Data Types
Tabular
Tasks
Classification
Feature Types
Continuous

Features

NameRoleTypeUnitsMissing ValuesDescription

Introductory Paper

Early prediction of gallstone disease with a machine learning-based method from bioimpedance and laboratory data
Irfan Esen, Hilal Arslan, Selin Aktürk Esen, Mervenur Gülşen, Nimet Kültekin, Oğuzhan Özdemir. 2024.
Medicine

Additional Metadata

Authors
Irfan Esen
Hilal Arslan
Selin Aktürk
Mervenur Gülşen
Nimet Kültekin
Oğuzhan Özdemir
Year Created
2024
License
CC BY 4.0