A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data (2025)
- Authors:
- USP affiliated authors: BENSEÑOR, ISABELA JUDITH MARTINS - FM ; ALENCAR, AIRLANE PEREIRA - IME ; PEREIRA, ALEXANDRE DA COSTA - FM ; GOULART, ALESSANDRA CARVALHO - FSP ; LOTUFO, PAULO ANDRADE - FM ; SANTOS, ITAMAR DE SOUZA - FM ; AMORIM, JÔNATAS LIMA DE - FM
- Unidades: FM; IME; FSP
- DOI: 10.1590/1414-431X2025e14986
- Subjects: APRENDIZADO COMPUTACIONAL; DOENÇAS CARDIOVASCULARES; ESTATÍSTICA APLICADA
- Keywords: Atherosclerotic cardiovascular disease prediction; Diabetes; Coronary artery calcification; Machine learning; Supervised learning
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Ribeirão Preto
- Date published: 2025
- Source:
- Título: Brazilian Journal of Medical and Biological Research
- ISSN: 0100-879X
- Volume/Número/Paginação/Ano: v. 58, artigo n. e14986, p. 1-8, 2025
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
AMORIM, Jonatas Lima de et al. A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data. Brazilian Journal of Medical and Biological Research, v. 58, n. artigo e14986, p. 1-8, 2025Tradução . . Disponível em: https://doi.org/10.1590/1414-431X2025e14986. Acesso em: 09 fev. 2026. -
APA
Amorim, J. L. de, Bensenor, I., Alencar, A. P., Pereira, A. da C., Goulart, A. C., Lotufo, P. A., & Santos, I. de S. (2025). A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data. Brazilian Journal of Medical and Biological Research, 58( artigo e14986), 1-8. doi:10.1590/1414-431X2025e14986 -
NLM
Amorim JL de, Bensenor I, Alencar AP, Pereira A da C, Goulart AC, Lotufo PA, Santos I de S. A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data [Internet]. Brazilian Journal of Medical and Biological Research. 2025 ; 58( artigo e14986): 1-8.[citado 2026 fev. 09 ] Available from: https://doi.org/10.1590/1414-431X2025e14986 -
Vancouver
Amorim JL de, Bensenor I, Alencar AP, Pereira A da C, Goulart AC, Lotufo PA, Santos I de S. A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data [Internet]. Brazilian Journal of Medical and Biological Research. 2025 ; 58( artigo e14986): 1-8.[citado 2026 fev. 09 ] Available from: https://doi.org/10.1590/1414-431X2025e14986 - Carotid intima-media thickness and incident hypertension: the Brazilian Longitudinal Study of Adult Health
- Low impact of traditional risk factors on carotid intima-media thickness: the ELSA-Brasil cohort
- Predictors of long-term survival among first-ever ischemic and hemorrhagic stroke in a Brazilian stroke cohort
- The influence of the day of the week of hospital admission on the prognosis of stroke patients
- Educational levels and the functional dependence of ischemic stroke survivors
- Multimorbidities are associated to lower survival in ischaemic stroke: results from a Brazilian Stroke Cohort (EMMA Study)
- Work-family conflict and ideal cardiovascular health score in ELSA-Brasil baseline assessment
- Burden of disease in Brazil, 1990-2016: a systematic subnational analysis for the Global Burden of Disease Study 2016
- The association between mood and anxiety disorders, and coronary heart disease in Brazil: a cross-sectional analysis on the Brazilian longitudinal study of adult health (ELSA-Brasil)
- Gender aspects of the relationship between migraine and cardiovascular risk factors: across-sectional evaluation in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)
Informações sobre o DOI: 10.1590/1414-431X2025e14986 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
| 3263755_-_A_machine_learn... | Direct link | ||
| 3263755_va_-_A_machine_le... | Direct link |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
