Machine learning algorithms as new screening approach for patients with endometriosis.

ABSTRACT: Endometriosis-a systemic and chronic condition occurring in women of childbearing age-is a highly enigmatic disease with unresolved questions. While multiple biomarkers, genomic analysis, questionnaires, and imaging techniques have been advocated as screening and triage tests for endometriosis to replace diagnostic laparoscopy, none have been implemented routinely in clinical practice. We investigated the use of machine learning algorithms (MLA) in the diagnosis and screening of endometriosis based on 16 key clinical and patient-based symptom features. The sensitivity, specificity, F1-score and AUCs of the MLA to diagnose endometriosis in the training and validation sets varied from 0.82 to 1, 0-0.8, 0-0.88, 0.5-0.89, and from 0.91 to 0.95, 0.66-0.92, 0.77-0.92, respectively. Our data sugg

SUBMITTER: Bendifallah S 

PROVIDER: S-EPMC8755739 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature