Application of intelligent algorithms in Down syndrome screening during second trimester pregnancy

作     者：Hong-Guo Zhang Yu-Ting Jiang Si-Da Dai Ling Li Xiao-Nan Hu Rui-Zhi Liu 

作者机构：Center for Reproductive Medicine and Center for Prenatal DiagnosisFirst HospitalJilin UniversityChangchun 130021Jilin ProvinceChina College of Communication EngineeringJilin UniversityChangchun 130012Jilin ProvinceChina 

出 版 物：《World Journal of Clinical Cases》 (世界临床病例杂志)

年 卷 期：2021年第9卷第18期

页      面：4573-4584页

核心收录：

学科分类：1002[医学-临床医学] 100211[医学-妇产科学] 10[医学] 

基　　金：Supported by Science and Technology Department of Jilin Province No.20190302073GX 

主　　题：Down syndrome Prenatal screening Algorithms Classification and regression tree Support vector machine Risk cutoff value 

摘      要：BACKGROUND Down syndrome(DS)is one of the most common chromosomal aneuploidy *** screening and diagnostic tests can aid the early diagnosis,appropriate management of these fetuses,and give parents an informed choice about whether or not to terminate a *** recent years,investigations have been conducted to achieve a high detection rate(DR)and reduce the false positive rate(FPR).Hospitals have accumulated large numbers of screened ***,artificial intelligence methods are rarely used in the risk assessment of prenatal screening for *** To use a support vector machine algorithm,classification and regression tree algorithm,and AdaBoost algorithm in machine learning for modeling and analysis of prenatal DS *** The dataset was from the Center for Prenatal Diagnosis at the First Hospital of Jilin *** designed and developed intelligent algorithms based on the synthetic minority over-sampling technique(SMOTE)-Tomek and adaptive synthetic sampling over-sampling techniques to preprocess the dataset of prenatal screening *** machine learning model was then ***,the feasibility of artificial intelligence algorithms in DS screening evaluation is *** The database contained 31 DS diagnosed cases,accounting for 0.03%of all *** dataset showed a large difference between the numbers of DS affected and non-affected cases.A combination of over-sampling and undersampling techniques can greatly increase the performance of the algorithm at processing non-balanced *** the number of iterations increases,the combination of the classification and regression tree algorithm and the SMOTETomek over-sampling technique can obtain a high DR while keeping the FPR to a *** The support vector machine algorithm and the classification and regression tree algorithm achieved good results on the DS screening *** the T21 risk cutoff value was set to 270,machine learning m