Abstract
Predicting pregnancy and live births using machine learning in the field of in-vitro fertilization (IVF) has long posed a significant challenge due to the difficulty in achieving consistent performance across various studies. In this paper, we conduct a comprehensive review and analysis of the existing limitations in current research. Additionally, we introduce a standardized machine learning pipeline, which serves as a valuable guide for future researchers. Furthermore, we propose two alternative modeling approaches: phase-by-phase modeling and subgroup FMLR modeling. These two alternatives not only enhance prediction performance but also offer clinically sensible explanations and timely guidance for users. Most notably, they shed light on the complexities of the IVF cycle, highlighting when, who, and where machine learning tasks face their greatest challenges. This insight can inspire future efforts in data collection and patient engagement processes.
Keywords: In-vitro fertilization; Machine Learning; Explainable AI; Medical AI
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