Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization.
<h4>Introduction</h4>Assisted reproductive technology has been proposed for women with infertility. Moreover, in vitro fertilization (IVF) cycles are increasing. Factors contributing to successful pregnancy have been widely explored. In this study, we used machine learning algorithms to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0267554 |
| _version_ | 1811289741678608384 |
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| author | Cheng-Wei Wang Chao-Yang Kuo Chi-Huang Chen Yu-Hui Hsieh Emily Chia-Yu Su |
| author_facet | Cheng-Wei Wang Chao-Yang Kuo Chi-Huang Chen Yu-Hui Hsieh Emily Chia-Yu Su |
| author_sort | Cheng-Wei Wang |
| collection | DOAJ |
| description | <h4>Introduction</h4>Assisted reproductive technology has been proposed for women with infertility. Moreover, in vitro 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.<h4>Materials and methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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. |
| first_indexed | 2024-04-13T04:00:52Z |
| format | Article |
| id | doaj.art-43c8decf5e994f49ae8942b2ea80e46d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T04:00:52Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-43c8decf5e994f49ae8942b2ea80e46d2022-12-22T03:03:29ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01176e026755410.1371/journal.pone.0267554Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization.Cheng-Wei WangChao-Yang KuoChi-Huang ChenYu-Hui HsiehEmily Chia-Yu Su<h4>Introduction</h4>Assisted reproductive technology has been proposed for women with infertility. Moreover, in vitro 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.<h4>Materials and methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.https://doi.org/10.1371/journal.pone.0267554 |
| spellingShingle | Cheng-Wei Wang Chao-Yang Kuo Chi-Huang Chen Yu-Hui Hsieh Emily Chia-Yu Su Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. PLoS ONE |
| title | Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. |
| title_full | Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. |
| title_fullStr | Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. |
| title_full_unstemmed | Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. |
| title_short | Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. |
| title_sort | predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization |
| url | https://doi.org/10.1371/journal.pone.0267554 |
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