Abstract: heart disease remains the leading cause of mortality worldwide, accounting for millions of deaths annually. Despite medical advancements, the early detection and accurate prediction of ...

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for millions of deaths each year according to the World Health Organization (WHO). Early detection of these diseases ...

For years, body mass index (BMI) has been the standard tool for assessing obesity and cardiovascular risk. But new research suggests (yet again) it may be missing people who are at real risk of heart ...

Share on Pinterest A new study found that waist-to-height ratio was closely linked to heart disease risk than BMI or waist circumference. Ableimages/Getty Images A new study suggests your body shape ...

The contributions of this study are summarized as follows: • We propose a hybrid stacked ensemble framework for cardiovascular disease prediction that combines TabNet, a deep neural network designed ...

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Heart failure with preserved ejection fraction (HFpEF) has become a major health issue because of its high mortality, high heterogeneity, and poor prognosis. Using genomic data to classify patients ...

Heart Disease Prediction: A Comparative Analysis of SVM, MLP, and Random Forest Models with Feature Selection This project focuses on developing and evaluating machine learning models to classify ...

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, most importantly whole-genome ...