Researchers have been using artificial intelligence (AI) techniques such as machine learning to predict cardiovascular disease (CVD) and identify patients who are at risk. AI algorithms can analyze large amounts of data and identify patterns that are difficult or impossible for humans to detect. This includes data from electronic health records, medical imaging tests, and genetic data.
According to a new Rutgers study, researchers may be able to predict cardiovascular disease in patients, such as arterial fibrillation and heart failure, by using artificial intelligence (AI) to examine the genes in their DNA.
“We predicted the association of highly significant cardiovascular disease genes tied to demographic variables like race, gender, and age with the successful execution of our model,” said Zeeshan Ahmed, a core faculty member at the Rutgers Institute for Health, Health Care Policy, and Aging Research (IFH) and lead author of the study published in Genomics.
The World Health Organization reports that cardiovascular disease is the leading cause of death worldwide, but it is estimated that more than 75 percent of premature cardiovascular disease is preventable. Approximately 45 percent of all cardiovascular disease deaths are caused by atrial fibrillation and heart failure.
We predicted the association of highly significant cardiovascular disease genes tied to demographic variables like race, gender, and age with the successful execution of our model.
Zeeshan Ahmed
Despite significant advances in cardiovascular disease diagnostics, prevention, and treatment, approximately half of those diagnosed die within five years for a variety of reasons. This includes both genetic and environmental factors. According to the researchers, the use of AI and machine learning can speed up our ability to identify genes with important implications for cardiovascular disease, leading to improvements in diagnosis and treatment.
Researchers from IFH analyzed 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 people by lowering the high risk of mortality and improving quality of life,” said Ahmed, an assistant professor in the Department of Medicine at Rutgers Robert Wood Johnson Medical School.
Future research should extend this approach by analyzing the entire set of genes in patients with cardiovascular disease, which may reveal important biomarkers and risk factors associated with cardiovascular disease susceptibility, according to the researchers.
These studies and others show that AI has the potential to improve the accuracy and efficiency of CVD risk prediction, leading to earlier diagnosis and treatment and ultimately better patient outcomes.
