Abstract
This study investigates the correlation between female reproductive health parameters and Autism Spectrum Disorder (ASD) prevalence from 2000 to 2024. The analysis used advanced statistical and machine learning models to identify trends in key reproductive indicators and their association with ASD prevalence. Significant positive correlations were observed between ASD prevalence and maternal age, while negative correlations were found with antral follicle count, Anti-Müllerian Hormone (AMH) levels, and fertility rate. The Random Forest model emerged as the most accurate predictive tool, explaining 96.9% of the variance in ASD prevalence. Maternal age was the dominant predictor of the variables analyzed, contributing approximately 75% of the model's predictive power, while estradiol levels and Follicle Stimulating Hormone (FSH) contributed significantly less. These findings highlight potential statistical associations but do not establish causality. Further research is necessary to validate these associations and explore underlying biological mechanisms.