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Machine learning may have advantages over the conventional approaches to improve discrimination by analysing complex interactions among predictors. We developed predictive models of attrition applying a conventional regression model and different machine learning methods. A total of 542 very preterm (&lt;\u200932 gestational weeks) infants born in Portugal as part of the European Effective Perinatal Intensive Care in Europe (EPICE) cohort were included. We tested a model with a fixed number of predictors (Baseline) and a second with a dynamic number of variables added from each follow-up (Incremental). Eight classification methods were applied: AdaBoost, Artificial Neural Networks, Functional Trees, J48, J48Consolidated, K-Nearest Neighbours, Random Forest and Logistic Regression. Performance was compared using AUC- PR (Area Under the Curve\u2014Precision Recall), Accuracy, Sensitivity and F-measure. Attrition at the four follow-ups were, respectively: 16%, 25%, 13% and 17%. Both models demonstrated good predictive performance, AUC-PR ranging between 69 and 94.1 in Baseline and from 72.5 to 97.1 in Incremental model. Of the whole set of methods, Random Forest presented the best performance at all follow-ups [AUC-PR<jats:sub>1<\/jats:sub>: 94.1 (2.0); AUC-PR<jats:sub>2<\/jats:sub>: 91.2 (1.2); AUC-PR<jats:sub>3<\/jats:sub>: 97.1 (1.0); AUC-PR<jats:sub>4<\/jats:sub>: 96.5 (1.7)]. Logistic Regression performed well below Random Forest. The top-ranked predictors were common for both models in all follow-ups: birthweight, gestational age, maternal age, and length of hospital stay. Random Forest presented the highest capacity for prediction and provided interpretable predictors. Researchers involved in cohorts can benefit from our robust models to prepare for and prevent loss to follow-up by directing efforts toward individuals at higher risk.<\/jats:p>","DOI":"10.1038\/s41598-022-13946-z","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T18:03:01Z","timestamp":1655920981000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine learning methods to predict attrition in a population-based cohort of very preterm infants"],"prefix":"10.1038","volume":"12","author":[{"given":"Raquel","family":"Teixeira","sequence":"first","affiliation":[]},{"given":"Carina","family":"Rodrigues","sequence":"additional","affiliation":[]},{"given":"Carla","family":"Moreira","sequence":"additional","affiliation":[]},{"given":"Henrique","family":"Barros","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Camacho","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"13946_CR1","first-page":"82","volume":"36","author":"L Marcellus","year":"2004","unstructured":"Marcellus, L. 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