Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization.

ABSTRACT:

Introduction

Assisted reproductive technology has been proposed for women with infertility. Moreover, in vitro fertilization (IVF) cycles are increasing. Factors contributing to successful pregnancy have been widely explored. In this study, we used machine learning algorithms to construct prediction models for clinical pregnancies in IVF.

Materials and methods

A total of 24,730 patients entered IVF and intracytoplasmic sperm injection cycles with clinical pregnancy outcomes at Taipei Medical University Hospital. Data used included patient characteristics and treatment. We used machine learning methods to develop prediction models for clinical pregnancy and explored how each variable affects the outcome of interest using partial dependence plots.

Results

Experimental r

SUBMITTER: Wang CW 

PROVIDER: S-EPMC9176781 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature