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

ABSTRACT: This study aimed to predict the likelihood of natural conception among couples by using a machine learning (ML) approach based on sociodemographic and sexual health data. This marks a novel, non-invasive methodology for fertility prediction. This prospective study included 197 couples divided into two groups: 98 fertile couples (Group 1) who achieved natural conception within one year, and 99 infertile couples (Group 2) who were unable to conceive despite regular unprotected intercourse. Data were collected using a structured form capturing 63 variables from both partners. Using the Permutation Feature Importance method, 25 key predictors were selected. The variables included BMI, age, menstrual cycle characteristics, and varicocele presence. Five ML models were developed and their perform

SUBMITTER: Kaya Y 

PROVIDER: S-EPMC12361258 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature