Retrospective Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jun 26, 2021; 9(18): 4573-4584
Published online Jun 26, 2021. doi: 10.12998/wjcc.v9.i18.4573
Application of intelligent algorithms in Down syndrome screening during second trimester pregnancy
Hong-Guo Zhang, Yu-Ting Jiang, Si-Da Dai, Ling Li, Xiao-Nan Hu, Rui-Zhi Liu
Hong-Guo Zhang, Yu-Ting Jiang, Xiao-Nan Hu, Rui-Zhi Liu, Center for Reproductive Medicine and Center for Prenatal Diagnosis, First Hospital, Jilin University, Changchun 130021, Jilin Province, China
Si-Da Dai, Ling Li, College of Communication Engineering, Jilin University, Changchun 130012, Jilin Province, China
Author contributions: Zhang HG and Dai SD contributed to data interpretation and manuscript writing; Jiang YT and Dai SD analyzed the data; Jiang YT and Hu XN contributed to data collection; Liu RZ and Li L contributed to the study design and reviewed the manuscript.
Supported by Science and Technology Department of Jilin Province, No. 20190302073GX.
Institutional review board statement: This study was approved by the Ethics Committee of the First Hospital of Jilin University, No. 2018-387.
Informed consent statement: Informed consent for this study was not requiredas the clinical data were anonymous.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: No additional data are available.
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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Rui-Zhi Liu, MD, Professor, Center for Reproductive Medicine and Center for Prenatal Diagnosis, First Hospital, Jilin University, No. 1 Xinmin Street, Chaoyang District, Changchun 130021, Jilin Province, China. lrz410@126.com
Received: July 22, 2020
Peer-review started: July 22, 2020
First decision: December 21, 2020
Revised: December 25, 2020
Accepted: March 10, 2021
Article in press: March 10, 2021
Published online: June 26, 2021
Processing time: 312 Days and 8.2 Hours
Core Tip

Core Tip: Down syndrome (DS) screening data tend to have a large overall data pool with a small proportion of positive cases. The use of data mining algorithms for these data can sufficiently mine the hidden correlation between natural information and patient outcomes and help doctors achieve the diagnosis of DS. This study used the support vector machine and classification and regression tree algorithms to construct a classification model for DS screening and achieved good results on the DS screening dataset.