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Development of a Web Application based on Machine Learning for screening esophageal varices in cirrhosis.
- Source :
-
La Tunisie medicale [Tunis Med] 2023 Aug-Sep; Vol. 101 (8-9), pp. 684-687. - Publication Year :
- 2023
-
Abstract
- Introduction: Esophageal varices (EV) are a common manifestation of portal hypertension in cirrhotic patients. Upper gastrointestinal endoscopy (UGE) is the gold standard for diagnosing EV. However, it is an invasive examination with a relatively high cost.<br />Aim: To develop a machine learning model for the prediction of EV in cirrhotic patients.<br />Methods: This is a cross-sectional observational study including all cirrhotic patients, for whom an UGE was performed, between January 2010 and December 2019. We adopted a structured methodical approach with reference to CRISP-DM (Cross-Industry Standard Process for Data Mining). The different steps carried out were: data collection and preparation, modelization, and deployment of the predictive models in a web application.<br />Results: We included 166 patients, 92 women (55.4%) and 74 men (44.6%). The mean age was 57.2 years. In UGE, 16 patients (9.6%) did not have EV. Other patients had EV grade 1 in 41 cases (24.7%), grade 2 in 81 cases (24.7%) and grade 3 in 28 cases (16.9%). After the selection phase, among the 36 initial variables, 19 were retained. Three machine learning models have been developed with a performance of 90%.<br />Conclusions: We developed a machine learning model combining several clinical and para-clinical variables for the prediction of EV in cirrhotic patients.
Details
- Language :
- English
- ISSN :
- 2724-7031
- Volume :
- 101
- Issue :
- 8-9
- Database :
- MEDLINE
- Journal :
- La Tunisie medicale
- Publication Type :
- Academic Journal
- Accession number :
- 38445402