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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

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A Novel Machine Learning Model for Predicting Natural Conception Using Non-Laboratory-Based Data.

Yeliz Kaya¹, Yunus Aydın², Coşkun Kaya³

¹Department of Gynecology and Obstetrics Nursing, Eskişehir Osmangazi University Faculty of Health Sciences, Eskişehir, Türkiye.

Reproductive Sciences (Thousand Oaks, Calif.)

|July 14, 2025

Summary

This study used machine learning (ML) to predict natural conception likelihood in couples. Key predictors included lifestyle and medical factors, but the model

Keywords:

Couple-based analysis Fertility prediction Machine learning Natural conception Sociodemographic factors

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Area of Science:

Reproductive Medicine
Artificial Intelligence in Healthcare
Biostatistics

Background:

Natural conception prediction traditionally relies on clinical assessments.
A novel, non-invasive approach using machine learning (ML) for fertility prediction is explored.
Sociodemographic and sexual health data offer potential for predictive modeling.

Purpose of the Study:

To develop and evaluate machine learning models for predicting the likelihood of natural conception in couples.
To identify key sociodemographic, lifestyle, and health predictors of natural conception.
To assess the feasibility of an AI-driven, couple-based approach to fertility assessment.

Main Methods:

Prospective study involving 197 couples (98 fertile, 99 infertile).
Data collection on 63 variables per partner, including BMI, age, and sexual health history.
Application of Permutation Feature Importance for predictor selection and development of five ML models.

Main Results:

Twenty-five key predictors were identified, including BMI, age, menstrual cycle characteristics, and varicocele presence.
The XGB Classifier model achieved 62.5% accuracy and a ROC-AUC of 0.580.
Important factors included BMI, caffeine intake, endometriosis history, and environmental exposures.

Conclusions:

Machine learning models show potential for predicting natural conception but currently have limited predictive capacity.
Couple-based and lifestyle factors are crucial predictors of natural conception.
Future research requires larger datasets and broader predictor variables to enhance AI accuracy in fertility assessment.