{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T14:25:46Z","timestamp":1792074346104,"version":"4.4.0"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010613","name":"Taipei Medical University Hospital","doi-asserted-by":"crossref","award":["108TMU-TMUH-15"],"award-info":[{"award-number":["108TMU-TMUH-15"]}],"id":[{"id":"10.13039\/501100010613","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Ministry of Science and Technology","award":["MOST 109-2221-E-038-018, MOST 110-2628-E-038-001"],"award-info":[{"award-number":["MOST 109-2221-E-038-018, MOST 110-2628-E-038-001"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Introduction<\/jats:title>\n<jats:p>Assisted reproductive technology has been proposed for women with infertility. Moreover, <jats:italic>in vitro<\/jats:italic> fertilization (IVF) cycles are increasing. Factors contributing to successful pregnancy have been widely explored. In this study, we used machine learning algorithms to construct prediction models for clinical pregnancies in IVF.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Materials and methods<\/jats:title>\n<jats:p>A total of 24,730 patients entered IVF and intracytoplasmic sperm injection cycles with clinical pregnancy outcomes at Taipei Medical University Hospital. Data used included patient characteristics and treatment. We used machine learning methods to develop prediction models for clinical pregnancy and explored how each variable affects the outcome of interest using partial dependence plots.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Experimental results showed that the random forest algorithm outperforms logistic regression in terms of areas under the receiver operating characteristics curve. The ovarian stimulation protocol is the most important factor affecting pregnancy outcomes. Long and ultra-long protocols have shown positive effects on clinical pregnancy among all protocols. Furthermore, total frozen and transferred embryos are positive for a clinical pregnancy, but female age and duration of infertility have negative effects on clinical pregnancy.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Our findings show the importance of variables and propensity of each variable by random forest algorithm for clinical pregnancy in the assisted reproductive technology cycle. This study provides a ranking of variables affecting clinical pregnancy and explores the effects of each treatment on successful pregnancy. Our study has the potential to help clinicians evaluate the success of IVF in patients.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0267554","type":"journal-article","created":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T17:34:08Z","timestamp":1654709648000},"page":"e0267554","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":33,"title":["Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization"],"prefix":"10.1371","volume":"17","author":[{"given":"Cheng-Wei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8867-5683","authenticated-orcid":true,"given":"Chao-Yang","family":"Kuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi-Huang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Hui","family":"Hsieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4801-5159","authenticated-orcid":true,"given":"Emily Chia-Yu","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"issue":"9","key":"pone.0267554.ref001","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1093\/humrep\/dew123","article-title":"Prevalence of infertility and help seeking among 15 000 women and men","volume":"31","author":"J Datta","year":"2016","journal-title":"Hum Reprod"},{"issue":"12","key":"pone.0267554.ref002","doi-asserted-by":"crossref","first-page":"e1001356","DOI":"10.1371\/journal.pmed.1001356","article-title":"National, Regional, and Global Trends in Infertility Prevalence Since 1990: A Systematic Analysis of 277 Health Surveys","volume":"9","author":"MN Mascarenhas","year":"2012","journal-title":"Plos Medicine"},{"issue":"11","key":"pone.0267554.ref003","doi-asserted-by":"crossref","first-page":"e3075","DOI":"10.1097\/MD.0000000000003075","article-title":"Chinese Herbal Products for Female Infertility in Taiwan: A Population-Based Cohort Study","volume":"95","author":"YC Hung","year":"2016","journal-title":"Medicine (Baltimore)"},{"issue":"3","key":"pone.0267554.ref004","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/S0015-0282(02)04694-0","article-title":"Conception, early pregnancy loss, and time to clinical pregnancy: a population-based prospective study","volume":"79","author":"X Wang","year":"2003","journal-title":"Fertil Steril"},{"issue":"7","key":"pone.0267554.ref005","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1007\/s10815-019-01498-7","article-title":"An artificial neural network for the prediction of assisted reproduction outcome","volume":"36","author":"P Vogiatzi","year":"2019","journal-title":"Journal of Assisted Reproduction and Genetics"},{"issue":"20","key":"pone.0267554.ref006","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine Learning in Medicine","volume":"132","author":"R.C. 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