---
title: >-
  Performance of machine learning algorithms for hysterectomy risk prediction
  among women with endometriosis in the United States.
description: >-
  Explore this publication on real-world evidence: Performance of machine
  learning algorithms for hysterectomy risk prediction among women with
  endometriosis in…
date: '2018-01-01'
category: Publications
tags:
  - ICPE
  - Approval & Commercialization
  - 'No'
  - Oral Presentation
  - R&D
canonical_url: >-
  https://www.headwaterscience.com/resources/publications/icpe/performance-of-machine-learning-algorithms-for-hysterectomy/
source: Headwater
license: © 2026 Headwater. All rights reserved.
slug: performance-of-machine-learning-algorithms-for-hysterectomy
id: 52PToMBNeAUOddlecFZOpW
contentType: article
---

## Challenge

Hysterectomy risk prediction in endometriosis patients could enable more personalized clinical management, but the performance of machine learning algorithms for this prediction task in a large real-world endometriosis cohort had not been evaluated.

## Solution

Target RWE researchers evaluated the performance of multiple machine learning algorithms for hysterectomy risk prediction among women with endometriosis using real-world clinical data, comparing ML approaches against traditional logistic regression.