AI Accurately Predicts Cardiovascular Disease by Examining Genes in DNA of Heart Patients

Cardiovascular disease is the world's leading cause of death, according to the World Health Organization, but it has been estimated that over 75% of premature cardiovascular diseases are preventable. Despite significant advances in diagnosis, prevention, and treatment for cardiovascular disease, about half of those affected still die within five years of diagnosis due to various factors, including genetic and environmental ones. By studying patients' DNA using artificial intelligence (AI), clinicians can now predict cardiovascular diseases such as atrial fibrillation and heart failure. With the help of AI, clinicians are able to identify genetic indicators of cardiovascular disease before symptoms even arise, potentially allowing for better prevention and treatment of this widespread condition.

A study by researchers at Rutgers University (New Brunswick, NJ, USA) suggests that machine learning and artificial intelligence can speed up the process of identifying genes associated with the most common types of cardiovascular disease. By analyzing data from healthy patients and those with existing diagnoses, AI and machine-learning models were used to identify genes which could have an impact on cardiovascular disease. This research has the potential to improve diagnosis and treatment of cardiovascular disease, including atrial fibrillation and heart failure, as well as other related diseases.

Image: Machine learning can be used to help clinicians with early diagnosis (Photo courtesy of Rutgers University)

The researchers identified and studied a set of genes that were significantly linked to cardiovascular disease. It was also discovered that age, gender, and race all had different correlations with heart failure and atrial fibrillation. For example, older patients were found to be more likely to have cardiovascular disease. Further research will be conducted in the future for examining the full set of genes in those suffering from cardiovascular disease, in order to discover any biomarkers or risk factors associated with increased susceptibility.

“With the successful execution of our model, we predicted the association of highly significant cardiovascular disease genes tied to demographic variables like race, gender and age,” said Zeeshan Ahmed, lead author of the study. “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.”

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Rutgers University