# Integrated Prediction System for Individualized Ovarian Stimulation and Ovarian Hyperstimulation Syndrome Prevention: Algorithm Development and Validation

## TL;DR

This study developed a machine learning tool to predict ovarian response and OHSS risk during fertility treatments, helping personalize FSH dosing for better outcomes.

## Contribution

The novel contribution is an integrated system that simultaneously predicts oocyte yield and OHSS risk across varying FSH doses using machine learning.

## Key findings

- Gradient boosting regressor predicted oocyte yield with high accuracy (R2=0.7978 internally, R2=0.7924 externally).
- LightGBM model achieved 0.7588 AUC for OHSS risk prediction in the internal dataset and 0.7287 in the external dataset.
- Key predictors identified include FSH starting dose to BMI ratio for oocyte yield and baseline antral follicle count for OHSS risk.

## Abstract

Accurately predicting ovarian response and determining the optimal starting dose of follicle-stimulating hormone (FSH) remain critical yet challenging for effective ovarian stimulation. Currently, there is a lack of a comprehensive model capable of simultaneously forecasting the number of oocytes retrieved (NOR) and assessing the risk of early-onset moderate-to-severe ovarian hyperstimulation syndrome (OHSS).

This study aimed to establish an integrated mode capable of forecasting the NOR and assessing the risk of early-onset moderate-to-severe OHSS across varying starting doses of FSH.

This prognostic study included patients undergoing their first ovarian stimulation cycles at 2 independent in vitro fertilization clinics. Automated classifiers were used for variable selection. Machine learning models (11 for NOR and 11 for OHSS) were developed and validated using internal (n=6401) and external (n=3805) datasets. Shapley additive explanation was applied for variable interpretation. The best-performing models were incorporated into a web-based prediction tool.

For NOR prediction, 17 variables were selected, with the gradient boosting regressor achieving the highest performance (internal dataset: R2=0.7978; external dataset: R2=0.7924). For OHSS prediction, 19 variables were identified, and the LightGBM model demonstrated superior performance (internal dataset: area under the receiver operating characteristic curve=0.7588; external dataset: area under the receiver operating characteristic curve=0.7287). Shapley additive explanation analysis highlighted the FSH starting dose to BMI ratio and baseline antral follicle count as key predictors for NOR and OHSS, respectively. Dose-response curves were generated to visualize predicted outcomes with varying FSH starting doses. The models were implemented in a user-friendly, research-oriented online prototype, individualized ovarian stimulation guide (InOvaSGuide).

This study introduces an integrated framework for predicting NOR and early-onset moderate-to-severe OHSS risk across different FSH doses. Future prospective evaluation is needed before clinical implementation.