Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms
Abstract Infertility has emerged as a significant global health concern. Assisted reproductive technology (ART) assists numerous infertile couples in conceiving, yet some experience repeated, unsuccessful cycles. This study aims to identify the pivotal clinical factors influencing the success of fre...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-88210-1 |
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| author | Zheng Yu Xiaoyan Zheng Jiaqi Sun Pengfei Zhang Ying Zhong Xingyu Lv Hongwen Yuan Fanrong Liang Dexian Wang Jie Yang |
| author_facet | Zheng Yu Xiaoyan Zheng Jiaqi Sun Pengfei Zhang Ying Zhong Xingyu Lv Hongwen Yuan Fanrong Liang Dexian Wang Jie Yang |
| author_sort | Zheng Yu |
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| description | Abstract Infertility has emerged as a significant global health concern. Assisted reproductive technology (ART) assists numerous infertile couples in conceiving, yet some experience repeated, unsuccessful cycles. This study aims to identify the pivotal clinical factors influencing the success of fresh embryo transfer of in vitro fertilization (IVF). We introduce a novel Non-negative Matrix Factorization (NMF)-based Ensemble algorithm (NMFE). By combining feature matrices from NMF, accelerated multiplicative updates for non-negative matrix factorization (AMU-NMF), and the generalized deep learning clustering (GDLC) algorithm. NMFE exhibits superior accuracy and reliability in analyzing the in vitro fertilization and embryo transfer (IVF-ET) dataset. The dataset comprises 2238 cycles and 85 independent clinical features, categorized into 13 categories based on feature correlation. Subsequently, the NMFE model was trained and reached convergence. Then the features of 13 categories were sequentially masked to analyze their individual effects on IVF-ET live births. The NMFE analysis highlights the significant influence of therapeutic interventions, Embryo transfer outcomes, and ovarian response assessment on live births of IVF-ET. Therapeutic interventions, including ovarian stimulation protocols, ovulation stimulation drugs, and pre-and intra-stimulation cycle acupuncture play prominent roles. However, their impacts on the IVF-ET model are reduced, suggesting a potential synergistic effect when combined. Conversely, factors like basic information, diagnosis, and obstetric history have a lesser influence. The NMFE algorithm demonstrates promising potential in assessing the influence of clinical features on live births in IVF fresh embryo transfer. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-b1b65a4e9ee24c399ec78acbde6a9ebb2025-02-02T12:16:53ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-025-88210-1Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithmsZheng Yu0Xiaoyan Zheng1Jiaqi Sun2Pengfei Zhang3Ying Zhong4Xingyu Lv5Hongwen Yuan6Fanrong Liang7Dexian Wang8Jie Yang9School of Intelligent Medicine, Chengdu University of Traditional Chinese MedicineAcupuncture and Tuina School, Chengdu University of Traditional Chinese MedicineAcupuncture and Tuina School, Chengdu University of Traditional Chinese MedicineSchool of Intelligent Medicine, Chengdu University of Traditional Chinese MedicineTraditional Chinese Medicine Department, Sichuan Jinxin Xi’nan Women’s and Children’s HospitalTraditional Chinese Medicine Department, Sichuan Jinxin Xi’nan Women’s and Children’s HospitalSchool of Traditional Chinese Medicine, Capital Medical UniversityAcupuncture and Tuina School, Chengdu University of Traditional Chinese MedicineSchool of Intelligent Medicine, Chengdu University of Traditional Chinese MedicineAcupuncture and Tuina School, Chengdu University of Traditional Chinese MedicineAbstract Infertility has emerged as a significant global health concern. Assisted reproductive technology (ART) assists numerous infertile couples in conceiving, yet some experience repeated, unsuccessful cycles. This study aims to identify the pivotal clinical factors influencing the success of fresh embryo transfer of in vitro fertilization (IVF). We introduce a novel Non-negative Matrix Factorization (NMF)-based Ensemble algorithm (NMFE). By combining feature matrices from NMF, accelerated multiplicative updates for non-negative matrix factorization (AMU-NMF), and the generalized deep learning clustering (GDLC) algorithm. NMFE exhibits superior accuracy and reliability in analyzing the in vitro fertilization and embryo transfer (IVF-ET) dataset. The dataset comprises 2238 cycles and 85 independent clinical features, categorized into 13 categories based on feature correlation. Subsequently, the NMFE model was trained and reached convergence. Then the features of 13 categories were sequentially masked to analyze their individual effects on IVF-ET live births. The NMFE analysis highlights the significant influence of therapeutic interventions, Embryo transfer outcomes, and ovarian response assessment on live births of IVF-ET. Therapeutic interventions, including ovarian stimulation protocols, ovulation stimulation drugs, and pre-and intra-stimulation cycle acupuncture play prominent roles. However, their impacts on the IVF-ET model are reduced, suggesting a potential synergistic effect when combined. Conversely, factors like basic information, diagnosis, and obstetric history have a lesser influence. The NMFE algorithm demonstrates promising potential in assessing the influence of clinical features on live births in IVF fresh embryo transfer.https://doi.org/10.1038/s41598-025-88210-1Cluster ensemble algorithmIVF-ETInfluence factorsAcupunctureSynergistic effects |
| spellingShingle | Zheng Yu Xiaoyan Zheng Jiaqi Sun Pengfei Zhang Ying Zhong Xingyu Lv Hongwen Yuan Fanrong Liang Dexian Wang Jie Yang Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms Scientific Reports Cluster ensemble algorithm IVF-ET Influence factors Acupuncture Synergistic effects |
| title | Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms |
| title_full | Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms |
| title_fullStr | Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms |
| title_full_unstemmed | Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms |
| title_short | Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms |
| title_sort | critical factors influencing live birth rates in fresh embryo transfer for ivf insights from cluster ensemble algorithms |
| topic | Cluster ensemble algorithm IVF-ET Influence factors Acupuncture Synergistic effects |
| url | https://doi.org/10.1038/s41598-025-88210-1 |
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