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Multimodal machine learning framework for cardiovascular risk stratification in adult obesity: a cross-sectional study

2026-01-30Artigo de investigação Acesso aberto
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datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorRibeiro, Pedro
dc.contributor.authorMarques, João Alexandre Lobo
dc.contributor.authorBrandão, Marconi Pereira
dc.contributor.authorNeto, Octávio Barbosa
dc.contributor.authorLeite, Camila Ferreira
dc.contributor.authorRodrigues, Pedro Miguel
dc.date.accessioned2026-01-13T11:29:25Z
dc.date.available2026-01-13T11:29:25Z
dc.date.issued2026-01-30
dc.description.abstractBackground and objectives: Cardiovascular diseases account for approximately 80% of all deaths caused by known medical conditions, making them the leading cause of mortality worldwide. The resent study investigates the use of electrocardiogram (ECG) non-linear features and different topological medical features (heart rate, anthropometry, blood, glucose, and lipid profile, and heart rate variability) to discriminate between different Framingham Cardiovascular Risk Scale status groups in adult obesity using machine learning. Methods: We conducted a cross-sectional study between November 2023 and May 2024 in Fortaleza, Ceará, Brazil. Based on the Framingham Cardiovascular Risk Scale, patients were categorized into three cardiovascular risk groups: Low (22 participants), Moderate (14 participants), and High (17 participants). From ECG signals at two different positions (ECG_Down and ECG_UP), 27 non-linear features were extracted using multi-band analysis. Additionally, 42 medical features provided by physicians were included. From a pool of 19 machine learning classifiers, models were trained and tested within a nested leave-one-out cross-validation procedure using information solely from ECG, solely from medical features, and combining both (multimodal), respectively, to distinguish between Low vs. Moderate, Low vs. High, Moderate vs. High, and All vs. All. Results: The multimodal model resented the best results for every comparison group, reaching (1) 88.89% Accuracy and 0.8831 area under the curve (AUC) for Low vs. Moderate; (2) 97.44% Accuracy and 0.9706 AUC for Low vs. High; (3) 93.55% Accuracy and an AUC of 0.9412 for Moderate vs. High; (4) 86.79% Accuracy and 0.9346 AUC for All vs. All. Conclusions: The multimodal model outperformed single-source models in cardiovascular risk classification. ECG-derived non-linear features, especially from ECG_Down, were key drivers, with medical features adding complementary value. The results support its potential use in clinical triage and diagnosis.eng
dc.identifier.citationRibeiro, P., Marques, J. A. L., Brandão, M. P., & Neto, O. B. et al. (2026). Multimodal machine learning framework for cardiovascular risk stratification in adult obesity: a cross-sectional study. Exploratory Research and Hypothesis in Medicine, 11(1), Article e00037. https://doi.org/10.14218/erhm.2025.00037
dc.identifier.doi10.14218/erhm.2025.00037
dc.identifier.otherabe1d22a-e291-4dc9-8c5e-0d83210f06b5
dc.identifier.urihttp://hdl.handle.net/10400.14/56514
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectAdult obesity
dc.subjectCardiovascular risk
dc.subjectFramingham risk score
dc.subjectMachine-learning
dc.subjectMultimodal
dc.subjectNon-linear features
dc.titleMultimodal machine learning framework for cardiovascular risk stratification in adult obesity: a cross-sectional studyeng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.titleExploratory Research and Hypothesis in Medicine
oaire.citation.volume11
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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