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Datasets were built containing information on beneficiaries\u2019 effective use of their health plan, as well as their characteristics. Five machine learning algorithms were used, namely Random forest, Extra tree, Xgboost, Naive bayes and K-nearest neighbor. The K-nearest neighbor algorithm had a recall rate of 81.12%, 83.77% precision and an Area Under the Curve (AUC) value of 0.9045. The study also revealed that categorization occurs, on average, 8.11 months before a beneficiary entering, for the first time, a high-risk group, considering the dataset classification from January 2019 to June 2020. <\/jats:p>","DOI":"10.1177\/14604582241230384","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T19:47:10Z","timestamp":1706816830000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of high-risk beneficiaries in private healthcare insurance"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1965-5421","authenticated-orcid":false,"given":"Adauto","family":"Santos","sequence":"first","affiliation":[{"name":"Technology Center, State University of Maring\u00e1, Maring\u00e1, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8599-0776","authenticated-orcid":false,"given":"Gislaine Camila Lapasini","family":"Leal","sequence":"additional","affiliation":[{"name":"Technology Center, State University of Maring\u00e1, Maring\u00e1, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8532-2011","authenticated-orcid":false,"given":"Renato","family":"Balancieri","sequence":"additional","affiliation":[{"name":"Computer Science Collegiate, State University of Paran\u00e1, Apucarana, Brazil"}]}],"member":"179","published-online":{"date-parts":[[2024,2,1]]},"reference":[{"key":"bibr1-14604582241230384","unstructured":"WHO. 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