Despite significant advancements in cardiovascular disease diagnostics, prevention and treatment, about half of the affected patients reportedly die within five years of receiving a diagnosis because of a variety of reasons, including genetic and environmental factors.

Researchers said the use of AI and machine learning can accelerate our ability to identify genes that have important implications for cardiovascular disease, which can lead to improvements in diagnoses and treatment.

Researchers from IFH analysed healthy patients and patients diagnosed with cardiovascular disease and used AI and machine-learning models to investigate the genes known to be associated with the most common manifestations of cardiovascular disease, including atrial fibrillation and heart failure.

They identified a group of genes that were significantly associated with cardiovascular disease. Researchers also found significant differences among race, gender and age factors based on cardiovascular disease. While age and gender factors correlated to heart failure, age and race factors correlated to atrial fibrillation. For example, in the patients examined, the older the patient, the more likely they were to have cardiovascular disease.

"Timely understanding and precise treatment of cardiovascular disease will ultimately benefit millions of individuals by reducing the high risk for mortality and improving the quality of life," said Ahmed, who is an assistant professor with the Department of Medicine at Rutgers Robert Wood Johnson Medical School.

Researchers said future research should extend this approach by analysing the full set of genes in patients with cardiovascular disease which may reveal important biomarkers and risk factors associated with cardiovascular disease susceptibility.