Coronary heart disease (CHD) remains the leading cause of mortality worldwide, disproportionately affecting low- and middle-income countries where diagnostic resources are limited. Traditional statistical models often fail to deliver adequate predict...
BACKGROUND: Generative artificial intelligence (AI) has shown great potential in various fields, including health care. However, its application for developing patient education materials (PEMs), particularly for those with coronary heart disease (CH...
BACKGROUND: Risk of coronary heart disease (CHD) in a specific period of years can be assessed using scores calculated by models, such as pooled cohort equations (PCEs) and Framingham Risk Score. However, there are few studies on on-site estimation o...
BACKGROUND: Coronary heart disease (CHD) continues to be a leading cause of global morbidity and mortality, with patients undergoing percutaneous coronary intervention (PCI) facing a significant risk of recurrent cardiovascular events. While secondar...
This study integrated ancient Traditional Chinese Medicine (TCM) pulse diagnosis techniques with modern machine learning to advance contemporary medical diagnostics. A portable intelligent TCM pulse diagnostic device was developed using MEMS and CMOS...
OBJECTIVE: To develop and validate prediction models for peak oxygen uptake (VO₂peak) in patients with coronary heart disease (CHD) using submaximal cardiopulmonary exercise testing (CPET) indicators and deep learning methods.
Coronary heart disease (CHD) is a major cardiovascular disorder that poses significant threats to global health and is increasingly affecting younger populations. Its treatment and prevention face challenges such as high costs, prolonged recovery per...
BACKGROUND: Coronary Heart Disease (CHD) and Non-Alcoholic Fatty Liver Disease (NAFLD) share overlapping pathogenic mechanisms including adipose tissue dysfunction, insulin resistance, and systemic inflammation mediated by adipokines. However, the sp...
Anais da Academia Brasileira de Ciencias
Jun 16, 2025
This research focuses on predicting cardiovascular disease using machine learning classification strategies. The study presents a unique approach by integrating multiple machine learning techniques, leveraging the strengths of Random Forest and Gradi...
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