Publication Name	World Journal of Methodology
Manuscript ID	88365
Country	Qatar

Received	2023-09-21 18:31
Peer-Review Started	2023-09-21 18:34
To Make the First Decision
Return for Revision	2023-09-29 03:06
Revised	2023-09-30 15:30
Second Decision	2023-10-31 02:42
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2023-11-03 08:09
Articles in Press	2023-11-03 08:09
Publication Fee Transferred
Edit the Manuscript by Language Editor	2023-10-30 05:05
Typeset the Manuscript	2023-12-14 05:19
Publish the Manuscript Online	2023-12-20 14:52

ISSN	2222-0682 (online)
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Category	Methodology
Manuscript Type	Editorial
Article Title	Challenges and limitations of synthetic minority oversampling techniques in machine learning
Manuscript Source	Invited Manuscript
All Author List	Ibraheem M Alkhawaldeh, Ibrahem Albalkhi and Abdulqadir Jeprel Naswhan
ORCID	Author(s) ORCID Number Ibraheem M Alkhawaldeh http://orcid.org/0000-0002-0187-1583 Abdulqadir Jeprel Naswhan http://orcid.org/0000-0003-4845-4119
Funding Agency and Grant Number
Corresponding Author	Abdulqadir Jeprel Naswhan, MSc, RN, Director, Director, Research Scientist, Senior Lecturer, Senior Researcher, Nursing for Education and Practice Development, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa
Key Words	Machine learning; Class imbalance; Overfitting; Misdiagnosis
Core Tip	Addressing class imbalance in medical datasets, particularly in the context of machine learning applications, requires a cautious approach. While oversampling methods like synthetic minority oversampling technique are commonly used, it is crucial to recognize their limitations. They may introduce synthetic instances that do not accurately represent the minority class, potentially leading to overfitting and unreliable results in real-world medical scenarios. Instead, we can consider exploring alternative approaches such as Ensemble Learning-Based Methods like XGBoost and Easy Ensemble which have shown promise in mitigating bias and providing more robust performance. Collaborating with data science specialists and medical professionals to design and validate these techniques is essential to ensure their reliability and effectiveness in medical applications.
Publish Date	2023-12-20 14:52
Citation	Alkhawaldeh IM, Albalkhi I, Naswhan AJ. Challenges and limitations of synthetic minority oversampling techniques in machine learning. World J Methodol 2023; 13(5): 373-378
URL	https://www.wjgnet.com/2222-0682/full/v13/i5/373.htm
DOI	https://dx.doi.org/10.5662/wjm.v13.i5.373

Full Article (PDF)	WJM-13-373-with-cover.pdf
Full Article (Word)	WJM-13-373.docx
Manuscript File	88365_Auto_Edited-JJW-WangTQ-JLW.docx
Answering Reviewers	88365-Answering reviewers.pdf
Audio Core Tip	88365-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	88365-Conflict-of-interest statement.pdf
Copyright License Agreement	88365-Copyright license agreement.pdf
Peer-review Report	88365-Peer-review(s).pdf
Scientific Misconduct Check	88365-Bing-Wang JJ-2.png
Scientific Editor Work List	88365-Scientific editor work list.pdf
CrossCheck Report	88365-CrossCheck report.pdf