Work

Multimodal Machine Learning Framework for Cardiovascular Risk Stratification in Adult Obesity: A Cross-sectional Study

Year: 2025

Type: article

Abstract: Cardiovascular diseases account for approximately 80% of all deaths caused by known medical conditions, making them the leading cause of mortality worldwide. The present 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.

Source: Exploratory Research and Hypothesis in Medicine

DOI: 10.14218/erhm.2025.00037

Authors: Pedro Ribeiro, João Alexandre Lobo Marques, Marconi Pereira Brandão, Octávio Barbosa Neto, Camila Ferreira Leite, Pedro Miguel Rodrigues

Institutions: Universidade Católica Portuguesa, Centro de Biotecnologia e Química Fina, Universidade Federal do Ceará

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Fundação para a Ciência e a Tecnologia