Self-report symptom-based endometriosis prediction using machine learning.

ABSTRACT: Endometriosis is a chronic gynecological condition that affects 5-10% of reproductive age women. Nonetheless, the average time-to-diagnosis is usually between 6 and 10 years from the onset of symptoms. To shorten time-to-diagnosis, many studies have developed non-invasive screening tools. However, most of these studies have focused on data obtained from women who had/were planned for laparoscopy surgery, that is, women who were near the end of the diagnostic process. In contrast, our study aimed to develop a self-diagnostic tool that predicts the likelihood of endometriosis based only on experienced symptoms, which can be used in early stages of symptom onset. We applied machine learning to train endometriosis prediction models on data obtained via questionnaires from two groups of women:

SUBMITTER: Goldstein A 

PROVIDER: S-EPMC10073113 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature