Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study

doi:10.1186/s12894-024-01537-1

科研成果详情

题名	Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study
作者	Cao, Shun-shun1; Liu, Xiao-ming2; Song, Bo-tian2; Hu, Yang-yang2
发表日期	2024-07-29
发表期刊	BMC Urology 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Surgical sperm retrieval Clinical pregnancy Machine learning SHapley Additive exPlanation
其他关键词	MALE-INFERTILITY ; PREVALENCE ; INJECTION
摘要	BackgroundThe relationship between surgical sperm retrieval of different etiologies and clinical pregnancy is unclear. We aimed to develop a robust and interpretable machine learning (ML) model for predicting clinical pregnancy using the SHapley Additive exPlanation (SHAP) association of surgical sperm retrieval from testes of different etiologies.MethodsA total of 345 infertile couples who underwent intracytoplasmic sperm injection (ICSI) treatment with surgical sperm retrieval due to different etiologies from February 2020 to March 2023 at the reproductive center were retrospectively analyzed. The six machine learning (ML) models were used to predict the clinical pregnancy of ICSI. After evaluating the performance characteristics of the six ML models, the Extreme Gradient Boosting model (XGBoost) was selected as the best model, and SHAP was utilized to interpret the XGBoost model for predicting clinical pregnancies and to reveal the decision-making process of the model.ResultsCombining the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, brier score, and the area under the precision-recall (P-R) curve (AP), the XGBoost model has the best performance (AUROC: 0.858, 95% confidence interval (CI): 0.778-0.936, accuracy: 79.71%, brier score: 0.151). The global summary plot of SHAP values shows that the female age is the most important feature influencing the model output. The SHAP plot showed that younger age in females, bigger testicular volume (TV), non-tobacco use, higher anti-m & uuml;llerian hormone (AMH), lower follicle-stimulating hormone (FSH) in females, lower FSH in males, the temporary ejaculatory disorders (TED) group, and not the non-obstructive azoospermia (NOA) group all resulted in an increased probability of clinical pregnancy.ConclusionsThe XGBoost model predicts clinical pregnancies associated with testicular sperm retrieval of different etiologies with high accuracy, reliability, and robustness. It can provide clinical counseling decisions for patients with surgical sperm retrieval of various etiologies.
资助项目	Wenzhou Basic Scientific Research Project of China
出版者	BMC
ISSN	1471-2490
卷号	24 期号:1
DOI	10.1186/s12894-024-01537-1
页数	12
WOS类目	Urology & Nephrology
WOS研究方向	Urology & Nephrology
WOS记录号	WOS:001279438800005
收录类别	SCIE ; SCOPUS ; PUBMED
URL	查看原文
PubMed ID	39075422
SCOPUSEID	2-s2.0-85200008244
通讯作者地址	[Hu, Yang-yang]Wenzhou Med Univ, Affiliated Hosp 2, Reprod Med Ctr, Obstet & Gynecol, Wenzhou 325000, Zhejiang, Peoples R China.
Scopus学科分类	Reproductive Medicine;Urology
SCOPUS_ID	SCOPUS_ID:85200008244
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/216876
专题	附属第二医院附属第二医院_生殖医学中心第二临床医学院、附属第二医院、育英儿童医院
通讯作者	Hu, Yang-yang
作者单位	1.Wenzhou Med Univ, Affiliated Hosp 2, Pediat Endocrinol Genet & Metab, Wenzhou 325000, Zhejiang, Peoples R China; 2.Wenzhou Med Univ, Affiliated Hosp 2, Reprod Med Ctr, Obstet & Gynecol, Wenzhou 325000, Zhejiang, Peoples R China
第一作者单位	附属第二医院; 第二临床医学院，附属第二医院、育英儿童医院
通讯作者单位	附属第二医院; 第二临床医学院，附属第二医院、育英儿童医院; 生殖医学中心
第一作者的第一单位	附属第二医院
推荐引用方式 GB/T 7714	Cao, Shun-shun,Liu, Xiao-ming,Song, Bo-tian,et al. Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study[J]. BMC Urology,2024,24(1).
APA	Cao, Shun-shun, Liu, Xiao-ming, Song, Bo-tian, & Hu, Yang-yang. (2024). Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study. BMC Urology, 24(1).
MLA	Cao, Shun-shun,et al."Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study".BMC Urology 24.1(2024).