{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:35:30Z","timestamp":1761165330837,"version":"build-2065373602"},"reference-count":19,"publisher":"Sociedade Brasileira de Computa\u00e7\u00e3o - SBC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"Este estudo utilizou minera\u00e7\u00e3o de dados para classificar indiv\u00edduos saud\u00e1veis e hipertensos com doen\u00e7as cardiovasculares (HA + DCV) no Brasil, a partir da PNS 2019. Foram testados algoritmos como \u00c1rvore de Decis\u00e3o, Floresta Aleat\u00f3ria e Naive-Bayes. Os modelos tiveram desempenho semelhante, com a Floresta Aleat\u00f3ria atingindo 97% de precis\u00e3o e sensibilidade para identificar saud\u00e1veis. No entanto, a classifica\u00e7\u00e3o de HA + DCV foi desafiadora, com menor sensibilidade, possivelmente devido \u00e0 aus\u00eancia de diagn\u00f3sticos formais e fatores como estilo de vida. Os resultados evidenciam a import\u00e2ncia de dados mais detalhados e longitudinais para melhorar a identifica\u00e7\u00e3o de doen\u00e7as cr\u00f4nicas.<\/jats:p>","DOI":"10.5753\/sbbd.2025.247058","type":"proceedings-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T19:26:36Z","timestamp":1761074796000},"page":"168-181","source":"Crossref","is-referenced-by-count":0,"title":["Minera\u00e7\u00e3o de Dados para Caracterizar Indiv\u00edduos Hipertensos com Doen\u00e7as Cardiovasculares no Brasil"],"prefix":"10.5753","author":[{"given":"Gustavo","family":"Costa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis Enrique Z\u00e1rate","family":"G\u00e1lvez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3742","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"AlKaabi, L., Ahmed, L., Al Attiyah, M., and Abdel-Rahman, M. 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Minera\u00e7\u00e3o de dados no diagn\u00f3stico de hipertens\u00e3o baseado na pesquisa nacional em sa\u00fade 2019. J Health Inform, 16(Especial).","DOI":"10.59681\/2175-4411.v16.iEspecial.2024.1250"},{"key":"6","doi-asserted-by":"crossref","unstructured":"G\u00e1rate-Escamila, A., El Hassani, A., and Andr\u00e8s, E. (2020). Classification models for heart disease prediction using feature selection and pca. Informatics in Medicine Unlocked, 19:100330.","DOI":"10.1016\/j.imu.2020.100330"},{"key":"7","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, L., Franca, D., and Zarate, L. (2024). Relev\u00e2ncia do entendimento do dom\u00ednio de problema na constru\u00e7\u00e3o de modelos computacionais de aprendizado. In Anais do XVIII Brazilian e-Science Workshop, pages 135\u2013142, Porto Alegre, RS, Brasil. SBC.","DOI":"10.5753\/bresci.2024.240233"},{"key":"8","unstructured":"IBGE (2020). 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