Publication Name	World Journal of Clinical Cases
Manuscript ID	104379
Country	Türkiye

Received	2024-12-19 17:44
Peer-Review Started	2024-12-20 01:27
To Make the First Decision
Return for Revision	2025-03-12 06:44
Revised	2025-03-16 21:32
Second Decision	2025-04-11 02:41
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-04-11 07:51
Articles in Press	2025-04-11 07:51
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-05-05 11:13
Publish the Manuscript Online	2025-05-14 05:24

ISSN	2307-8960 (online)
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/
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Category	Surgery
Manuscript Type	Case Control Study
Article Title	Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model
Manuscript Source	Invited Manuscript
All Author List	Zeynep Kucukakcali and Sami Akbulut
ORCID	Author(s) ORCID Number Zeynep Kucukakcali http://orcid.org/0000-0001-7956-9272 Sami Akbulut http://orcid.org/0000-0002-6864-7711
Funding Agency and Grant Number
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
Key Words	Acute appendicitis; Complicated acute appendicitis; Machine learning; Stochastic gradient boosting
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.
Publish Date	2025-05-14 05:24
Citation	<p>Kucukakcali Z, Akbulut S. Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model. <i>World J Clin Cases</i> 2025; 13(22): 104379</p>
URL	https://www.wjgnet.com/2307-8960/full/v13/i22/104379.htm
DOI	https://dx.doi.org/10.12998/wjcc.v13.i22.104379

Full Article (PDF)	WJCC-13-104379-with-cover.pdf
STROBE Statement	104379-STROBE-statement.pdf
Manuscript File	104379_Auto_Edited_061358.docx
Answering Reviewers	104379-answering-reviewers.pdf
Audio Core Tip	104379-audio.mp3
Biostatistics Review Certificate	104379-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	104379-conflict-of-interest-statement.pdf
Copyright License Agreement	104379-copyright-assignment.pdf
Signed Informed Consent Form(s) or Document(s)	104379-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	104379-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	104379-non-native-speakers.pdf
Peer-review Report	104379-peer-reviews.pdf
Scientific Misconduct Check	104379-scientific-misconduct-check.png
Scientific Editor Work List	104379-scientific-editor-work-list.pdf
CrossCheck Report	104379-crosscheck-report.pdf