Retrospective Cohort Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Nov 26, 2023; 11(33): 7951-7964
Published online Nov 26, 2023. doi: 10.12998/wjcc.v11.i33.7951
Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes
Chung-Chi Yang, Chung-Hsin Peng, Li-Ying Huang, Fang Yu Chen, Chun-Heng Kuo, Chung-Ze Wu, Te-Lin Hsia, Chung-Yu Lin
Chung-Chi Yang, Division of Cardiovascular Medicine, Taoyuan Armed Forces General Hospital, Taoyuan City 32551, Taiwan
Chung-Chi Yang, Division of Cardiovascular, Tri-service General Hospital, Taipei City 114202, Taiwan
Chung-Hsin Peng, Department of Urology, Cardinal Tien Hospital, New Taipei City 23148, Taiwan
Chung-Hsin Peng, School of Medicine, Fu-Jen Catholic University, New Taipei City 242062, Taiwan
Li-Ying Huang, Department of Internal Medicine, Department of Medical Education, School of Medicine, Fu Jen Catholic University Hospital, New Taipei City 243, Taiwan
Li-Ying Huang, Chun-Heng Kuo, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 243, Taiwan
Fang Yu Chen, Department of Endocrinology, Fu Jen Catholic University Hospital, New Taipei City 243, Taiwan
Chun-Heng Kuo, Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei City 243, Taiwan
Chung-Ze Wu, Division of Endocrinology, Shuang Ho Hospital, New Taipei City 23561, Taiwan
Chung-Ze Wu, School of Medicine, Taipei Medical University, Taipei City 11031, Taiwan
Te-Lin Hsia, Department of Internal Medicine, Cardinal Tien Hospital, New Taipei City 23148, Taiwan
Chung-Yu Lin, Department of Cardiology, Fu Jen Catholic University Hospital, New Taipei City 24352, Taiwan
Chung-Yu Lin, Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Author contributions: Yang CC, Lin CY designed the research study; Huang LY, Chen FY and Hsia TL performed the research; Kuo CH and Wu CZ contributed new reagents and analytic tools; Yang CC, Peng CH and Lin CY analyzed the data and wrote the manuscript; All authors have read and approve the final manuscript.
Institutional review board statement: The study was reviewed and approved by the Cardinal Tien Hospital Institutional Review Board (Approval No. CTH-102-2-5-024).
Informed consent statement: Since this is a retrospective cohort study and we collected our data from the medical records of the hospital. Therefore, no informed consent was needed. This was approved by the IRB of the hospital.
Conflict-of-interest statement: There is no conflict of Interest in the current study.
Data sharing statement: The datasets generated and/or analyzed during the current study are not publicly available because they include other valuable information which could be used to produce additional papers, but are available from the corresponding author on reasonable request.
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.
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: Chung-Yu Lin, MD, Doctor, Department of Cardiology, Fu Jen Catholic University Hospital, No. 69 Guizi Road, Taishan District, New Taipei City 24352, Taiwan. a02076@mail.fjuh.fju.edu.tw
Received: August 4, 2023
Peer-review started: August 4, 2023
First decision: October 9, 2023
Revised: October 23, 2023
Accepted: November 13, 2023
Article in press: November 13, 2023
Published online: November 26, 2023
Processing time: 102 Days and 7.2 Hours
Core Tip

Core Tip: This is a retrospective study to use four machine learning methods to evaluate the impacts of demographic and biochemistry data to identify subjects with abnormal myocardial perfusion scan in Chinese type 2 diabetes. Our results showed that gender was the most important factor, followed by body mass index, age, LDL-cholesterol, glycated hemoglobin and smoking accordingly.