Clinical and Translational Research
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jul 26, 2024; 12(21): 4661-4672
Published online Jul 26, 2024. doi: 10.12998/wjcc.v12.i21.4661
Predicting depression in patients with heart failure based on a stacking model
Hui Jiang, Rui Hu, Yu-Jie Wang, Xiang Xie
Hui Jiang, Rui Hu, Xiang Xie, Department of Ultrasound Diagnosis, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
Yu-Jie Wang, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
Author contributions: Jiang H and Hu R designed the research study; Jiang H, Wang YJ and Xie X analyzed the data and wrote the manuscript. All of the authors read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that they have no competing interests.
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: Xiang Xie, Doctor, Chief Physician, Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Shushan District, Hefei 230601, Anhui Province, China. sonographer@126.com
Received: April 27, 2024
Revised: May 27, 2024
Accepted: June 17, 2024
Published online: July 26, 2024
Processing time: 63 Days and 19.3 Hours
Core Tip

Core Tip: In this study, we utilized easily accessible demographic data and laboratory indicators to construct a stacking ensemble model for predicting depression in heart failure patients. We then compared the performance of this stacking model with that of a single machine learning (ML) model and discovered that the stacking model outperformed the single ML model.