Predictive model of neurocognitive functioning after acute coronary syndrome. A machine learning approach
The variance in neurocognitive functioning is explained by a combination of biochemical indicators and body composition, reflecting classical cardiovascular risk factors and depression. The obtained RF-cv predictive model supports early identification of patients for tailored interventions.
Authors
- Miguel Peixoto (ORCID: https://orcid.org/0000-0002-6361-1066)
- Dulce Sousa (ORCID: https://orcid.org/0000-0002-2619-8444)
- Bruno Peixoto (ORCID: https://orcid.org/0000-0002-2427-6330)
- Afonso Rocha (ORCID: https://orcid.org/0000-0003-0824-4598)
- Inês Moreira (ORCID: https://orcid.org/0000-0003-2178-6033)
Institutions
- Universidade do Porto (PT)
- Hospital de São João (PT)
- Cooperativa de Ensino Superior Politécnico e Universitário (PT)
- Administração Regional de Saúde de Lisboa e Vale do Tejo (PT)
- Institute of Behavioral Sciences (JP)
- AgriBio (AU)
- Polytechnic Institute of Porto (PT)
Publication Details
- Journal
- Journal of Cardiovascular and Thoracic Research
- Published
- 2025-09-28
- DOI
- https://doi.org/10.34172/jcvtr.025.33340
- Primary Topic
- Cardiovascular Health and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 0.00