Evaluating the risk of endometriosis based on patients' self-assessment questionnaires.

ABSTRACT:

Background

Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered.

Methods

In this study, we employed machine learning algorithms to predict endometriosis based on the patient symptoms extracted from 13,933 questionnaires. We compared the results of feature selection obtained from various algorithms (i.e., Boruta algorithm, Recursive Feature Selection) with experts' decisions. As a benchmark model architecture, we utili

SUBMITTER: Zielinski K 

PROVIDER: S-EPMC10612251 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature