Case Control Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Aug 6, 2025; 13(22): 104379
Published online Aug 6, 2025. doi: 10.12998/wjcc.v13.i22.104379
Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model
Zeynep Kucukakcali, Sami Akbulut
Zeynep Kucukakcali, Sami Akbulut, Department of Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya 44280, Türkiye
Sami Akbulut, Surgery and Liver Transplant Institute, Inonu University Faculty of Medicine, Malatya 44280, Türkiye
Author contributions: Akbulut S and Kucukakcali Z collected data, analyzed statistical, wrote manuscript, projected development and reviewed final version.
Institutional review board statement: This study was reviewed and approved by the Inonu University institutional review board for non-interventional studies (Approval No. 2024/6809).
Informed consent statement: Not applicable, as this study was retrospective.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest regarding this study.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: There are no additional data available for this study.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Sami Akbulut, MD, PhD, Professor, Surgery and Liver Transplant Institute, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Türkiye. akbulutsami@gmail.com
Received: December 18, 2024
Revised: March 16, 2025
Accepted: April 11, 2025
Published online: August 6, 2025
Processing time: 146 Days and 17.7 Hours
Core Tip

Core Tip: This study uses an open access database and a Stochastic Gradient Boosting (SGB) machine learning algorithm to tell the difference between acute appendicitis (AAp) patients who are complicated and those who are not complicated. It also finds important biomarkers for both groups by using variable importance values that come from the modeling process. The SGB model demonstrated excellent precision in identifying AAp patients while exhibiting average performance in differentiating complicated AAp patients from uncomplicated ones.