Researchers use artificial intelligence to predict cardiovascular disease – ET HealthWorld


New York: According to a recent study, researchers can predict cardiovascular disease in individuals using artificial intelligence (AI) to look at the genes in your DNA. cardiovascular disease it includes things like atrial fibrillation and heart failure.

“With the successful run of our model, we predicted the association of highly significant cardiovascular disease genes linked to demographic variables such as race, gender and age,” he said. Zeeshan Ahmedsenior faculty member of the Rutgers Institute for Health, Health Care Policy and Aging Research (IFH) and lead author of the study, published in genomics.

According to the World Health Organization, cardiovascular diseases are the leading cause of death worldwide; however, it is estimated that more than 75% of premature cardiovascular diseases can be prevented. Atrial fibrillation and heart failure contribute to approximately 45 percent of all deaths from cardiovascular disease.

Despite significant advances in the diagnosis, prevention, and treatment of cardiovascular disease, approximately half of affected patients die within five years of receiving diagnosis due to a variety of reasons. including genetic and environmental factors. The researchers said that the use of AI and machine learning may accelerate our ability to identify genes that have important implications for cardiovascular disease, which may lead to improvements in diagnosis and treatment.

IFH researchers analyzed healthy patients and patients diagnosed with cardiovascular disease and used artificial intelligence and machine learning models to investigate 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. The researchers also found significant differences between factors of race, gender and age based on cardiovascular disease. While age and gender factors were correlated with HF, age and race factors were correlated with atrial fibrillation. For example, in the patients examined, the older the patient was, the more likely he was to have cardiovascular disease.

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“Timely understanding and accurate treatment of cardiovascular disease will ultimately benefit millions of people by reducing the high risk of mortality and improving quality of life,” said Ahmed, an assistant professor at the Department of Medicine at Rutgers roberto wood Johnson School of Medicine.

The researchers said that future research should expand this approach by analyzing the full set of genes in patients with cardiovascular disease that may reveal important biomarkers and risk factors associated with susceptibility to cardiovascular disease.



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