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 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.

Keywords: Acupuncture; Cluster ensemble algorithm; IVF-ET; Influence factors; Synergistic effects.

Fig. 1

Area diagram integrating ACC and Purity on the dataset IVF-ET. The algorithm becomes more efficient as the area increases. The NMFE algorithm has the largest area (Area: 3404.13), indicating that its effectiveness is the highest among the above algorithms.

Fig. 2

Rank each feature group according to the ACC-GAP and PUR-GAP values. The top 5 groups that have the greatest influence on the IVF-ET results identified are Therapeutic Interventions, Embryo Transfer Outcomes, Ovarian Response Assessment Indicators, Embryo Transfer-Related Indicators, and Complications During Pregnancy.

Fig. 3

Rank of different intervention factors in the IVF-ET model. Ovarian stimulation protocols, ovulation stimulation drugs, and pre-and intra-cycle acupuncture significantly declined in the rankings of IVF-ET models.

Fig. 4

Flowchart of patient inclusion.

Fig. 5

The clinical features in the dataset. All the clinical features were grouped into 13 categories and calculated the fraction of features in each group relative to the total number of features. Female Basic Information (5 items, 6%), Male Basic Information (5 items, 6%), Menstrual History (3 items, 4%), Obstetric History (12 items, 14%), Previous History of Assisted Reproduction (3 items, 14%), Ovarian Response Assessment Indicators (8 items, 9%), Therapeutic Interventions (4 items, 5%), Factors Associated with Embryo Quality (13 items, 15%), Female Diagnosis (10 items, 12%), Embryo Transfer-related Indicators (6 items, 7%), Hormone Levels After Transplantation (2 items, 2%), Embryo Transfer Outcomes (9 items, 11%), and Complications During Pregnancy (5 items, 6%).

Fig. 6

Algorithmic framework for NMFE. It is constructed by fusing the feature matrices that are obtained from NMF, AMU-NMF, and GDLC algorithms.