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©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Dec 24, 2022; 13(12): 967-979
Published online Dec 24, 2022. doi: 10.5306/wjco.v13.i12.967
Published online Dec 24, 2022. doi: 10.5306/wjco.v13.i12.967
Machine learning-assisted ensemble analysis for the prediction of urinary tract infection in elderly patients with ovarian cancer after cytoreductive surgery
Jiao Ai, Fang-Fang Zhou, Yi-Xiang Liao, Tao Yang, Department of Urology, Jingzhou Central Hospital, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou 434020, Hubei Province, China
Yao Hu, Department of Obstetrics and Gynaecology, Jingzhou Central Hospital, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou 434020, Hubei Province, China
Author contributions: Ai J and Hu Y contributed equally to this work; Yang T designed the research study; Ai J, Hu Y, Zhou FF, and Liao YX performed the research; All authors have read and approved the final manuscript.
Institutional review board statement: This study was approved by the Institutional Review Committee of Jingzhou Central Hospital (JZ-2022014).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrolment.
Conflict-of-interest statement: All authors declare that there are no conflicts of interest.
Data sharing statement: No additional data are available.
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: Tao Yang, MD, Surgical Oncologist, Department of Urology, Jingzhou Central Hospital, Jingzhou Hospital Affiliated to Yangtze University, No. 26 Chuyuan Road, Jingzhou 434020, Hubei Province, China. yangtaotao123321@163.com
Received: August 1, 2022
Peer-review started: August 1, 2022
First decision: November 11, 2022
Revised: November 17, 2022
Accepted: December 8, 2022
Article in press: December 8, 2022
Published online: December 24, 2022
Peer-review started: August 1, 2022
First decision: November 11, 2022
Revised: November 17, 2022
Accepted: December 8, 2022
Article in press: December 8, 2022
Published online: December 24, 2022
Core Tip
Core Tip: Using a machine learning-based algorithm, we developed a feasible and robust method to identify factors that are significant for predicting urinary tract infections. The random forest classifier was especially robust and can improve the prediction and early detection of urinary tract infections in patients with ovarian cancer. In addition, the five most crucial factors were age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia. Clinicians may find it extremely helpful to assess the individualised risk of urinary tract infections in clinical practice by incorporating the presentation of simple clinical data.