| Abstract |
This project provides a noninvasive, cost-effective endometriosis screening method by leveraging a multivariable predictive machine learning framework. By analyzing clinical data to determine diagnostic likelihood, the platform offers a low-barrier alternative to traditional invasive surgery. This data-driven approach empowers patient advocacy and accelerates specialist intervention, directly addressing the systemic dismissals and normalized symptoms that currently delay women's endometriosis diagnosis by a decade. |
