Abstract
BACKGROUND: Accurately predicting ovulation timing is critical for women undergoing natural cycle-frozen embryo transfer. However, the precise predicting of the ovulation timing remains challenging due to the lack of consensus among different clinics regarding the definition of this significant event. OBJECTIVE: To compare the effectiveness of preovulatory serum progesterone levels (P4) versus luteinizing hormone levels (LH) in predicting ovulation time using two machine learning models. METHODS: 771 patients who underwent autologous natural cycle-frozen embryo transfer between January 2015 and February 2022 were recruited. Utilizing variables including follicle diameters, preovulatory serum levels of LH, E2, and P4, two machine learning models were constructed to predict the ovulation time, the importance of the variables in predicting ovulation timing was further ranked. RESULTS: Two machine learning models have the capability to accurately predict the timing of ovulation, specifically within 72, 48, or 24 h. The overall accuracy rates of the validation dataset, as determined by the classification trees and random forest models, were found to be 78.83% and 85.28% respectively. Notably, when predicting ovulation within 24 h, the accuracy rate of P4 ≥ 0.65ng/ml exceeded 92%. Furthermore, it was important to consider LH or E2 levels in conjunction with P4 when assessing ovulation timing in cases where P4<0.65ng/ml. CONCLUSIONS: Preovulatory serum P4 levels are better predictors of ovulation timing than LH levels and could be used as an alternative in clinical settings, and the model we developed can be used to pinpoint the day of ovulation. Ongoing research and advancements in technology are anticipated to enhance and refine the ovulation method.