[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127505-en":3,"doc-seo-127505-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127505,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Assessment and quantification of ovarian reserve on the basis of machine learning models - Original research","Early detection of ovarian aging is critical, yet no ideal biomarker or universally accepted evaluation system exists. This multicenter nationwide study developed machine-learning prediction models to assess and quantify ovarian reserve by estimating ovarian age as equivalent to chronological age. Using LASSO feature selection, seven models (ANN, SVM, GLM, KNN, GBDT, XGBoost, LightGBM) were compared with Pearson correlation, MAE, and MSE. LightGBM ranked best overall and showed lowest errors, particularly in women aged 20–35 years.","TYPE Original Research PUBLISHED 15 March 2023  \nDOI 10.3389/fendo.2023.1087429  \nOPEN ACCESS  \nEDITED BY  \nAntonio Simone Laganà, University of Palermo, Italy  \nREVIEWED BY  \nAdriana Vita Streva, University of Palermo, Italy Jiaqiang Xiong,  \nZhongnan Hospital, Wuhan University, China  \n*CORRESPONDENCE Yan Li  \n [liyan@tjh.tjmu.edu.cn](liyan@tjh.tjmu.edu.cn)[ ](liyan@tjh.tjmu.edu.cn)Shixuan Wang  \n [shixuanwang@tjh.tjmu.edu.cn](shixuanwang@tjh.tjmu.edu.cn)  \n†These authors have contributed equally to this work and share first authorship  \nSPECIALTY SECTION  \nThis article was submitted to Endocrinology of Aging, a section of the journal Frontiers in Endocrinology  \nRECEIVED 02 November 2022  \nACCEPTED 24 February 2023  \nPUBLISHED 15 March 2023  \nCITATION  \nDing T, Ren W, Wang T, Han Y, Ma W, Wang M, Fu F, Li Y and Wang S (2023) Assessment and quantification of ovarian reserve on the basis of machine learning models.  \nFront. Endocrinol. 14:1087429 .  \ndoi: 10.3389/fendo.2023.1087429  \nCOPYRIGHT  \n© 2023 Ding, Ren, Wang, Han, Ma, Wang, Fu, Li and Wang. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAssessment and quantification of ovarian reserve on the basis of machine learning models  \nTing Ding †, Wu Ren †, Tian Wang †, Yun Han, Wenqing Ma, Man Wang, Fangfang Fu, Yan Li* and Shixuan Wang*  \nDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China  \nBackground: Early detection of ovarian aging is of huge importance, although no ideal marker or acknowledged evaluation system exists. The purpose of this study was to develop a better prediction model to assess and quantify ovarian reserve using machine learning methods.  \nMethods: This is a multicenter, nationwide population-based study including a total of 1,020 healthy women. For these healthy women, their ovarian reserve was quantified in the form of ovarian age, which was assumed equal to their chronological age, and least absolute shrinkage and selection operator (LASSO) regression was used to select features to construct models. Seven machine learning methods, namely artificial neural network (ANN), support vector machine (SVM), generalized linear model (GLM), K-nearest neighbors regression (KNN), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to construct prediction models separately. Pearson ’ s correlation coefficient (PCC), mean absolute error (MAE), and mean squared error (MSE) were used to compare the efficiency and stability of these models.  \nResults: Anti-Müllerian hormone (AMH) and antral follicle count (AFC) were detected to have the highest absolute PCC values of 0.45 and 0.43 with age and held similar age distribution curves. The LightGBM model was thought to be the most suitable model for ovarian age after ranking analysis, combining PCC, MAE, and MSE values. The LightGBM model obtained PCC values of 0 . 82, 0 . 56, and 0.70 for the training set, the test set, and the entire dataset, respectively. The LightGBM method still held the lowest MAE and cross-validated MSE values. Further, in two different age groups (20–35 and >35 years), the LightGBM model also obtained the lowest MAE value of 2.88 for women between the ages of 20 and 35 years and the second lowest MAE value of 5.12 for women over the age of 35 years.  \nConclusion: Machine learning methods combining multi-features were reliable in assessing and quantifying ovarian reserve, and the LightGBM method turned  \nFrontiers in Endocrin","cbCaicT5sKBPSmll","https://ap.wps.com/l/cbCaicT5sKBPSmll","pdf",1513054,2,1,9,"English","en",105,"# Background\n# Methods\n# Results\n## Model comparison\n## Subgroup performance\n# Conclusion\n# Introduction\n## STRAW+10 and current evaluation methods","[{\"question\":\"What is the goal of the study on ovarian reserve?\",\"answer\":\"To develop prediction models that assess and quantify ovarian reserve using machine learning, by estimating ovarian age as equivalent to chronological age.\"},{\"question\":\"How were the machine learning models built and compared?\",\"answer\":\"LASSO regression selected features, then seven models were constructed separately. Pearson correlation coefficient, MAE, and MSE were used to evaluate efficiency and stability.\"},{\"question\":\"Which model performed best overall and why?\",\"answer\":\"The LightGBM model ranked as the most suitable approach, showing strong correlation and the lowest MAE and cross-validated MSE among the compared methods.\"}]","Assessment and quantification of ovarian reserve on the basis of machine learning models - Original research | PDF",1785939517,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"assessment-and-quantification-of-ovarian-reserve-on-the-basis-of-machine-learning-models-original-research","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/assessment-and-quantification-of-ovarian-reserve-on-the-basis-of-machine-learning-models-original-research/127505/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the goal of the study on ovarian reserve?","Question",{"text":76,"@type":77},"To develop prediction models that assess and quantify ovarian reserve using machine learning, by estimating ovarian age as equivalent to chronological age.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the machine learning models built and compared?",{"text":81,"@type":77},"LASSO regression selected features, then seven models were constructed separately. 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