Artificial Intelligence Can Predict Cardiovascular Disease

According to a new study performed at Rutgers, Artificial Intelligence (AI) is of great help to scientists in predicting cardiovascular diseases like heart failure and arterial fibrillation in patients. Also, AI could analyze the genes present in their DNA.

Artificial Intelligence Proven Effective to Predict Cardiovascular Disease

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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.

Zeeshan Ahmed, Study Lead Author and Core Faculty Member, Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University

The study has been reported in the Genomics journal.

As per data gathered by the World Health Organization, cardiovascular disease is the chief cause of death throughout the world. However, it is evaluated that over 75% of premature cardiovascular disease is avoidable. Atrial fibrillation and heart failure add up to around 45% of all cardiovascular disease deaths.

In spite of considerable progress in cardiovascular disease diagnostics, prevention, and treatment, nearly half of the affected patients allegedly die within five years of receiving a diagnosis due to a variety of reasons such as environmental and genetic factors.

To determine genes that have significant implications for cardiovascular disease, scientists stated that the use of AI and machine learning could help expedite potential. This finding could result in improvements in diagnoses and treatment.

Scientists from IFH examined healthy patients as well as patients diagnosed with cardiovascular disease and made use of AI and machine-learning models to analyze the genes known to be linked to the most general manifestations of cardiovascular disease, such as heart failure and atrial fibrillation.

A group of genes was determined by the researchers to be considerably linked to having cardiovascular disease. Also, scientists discovered considerable variations among gender, race, and age factors based on cardiovascular disease.

While gender and age factors are associated with heart failure, age and race factors are correlated to atrial fibrillation. For instance, in the patients analyzed, the older the patient, the probability of them having a cardiovascular disease was higher. 

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.

Zeeshan Ahmed, Study Lead Author and Core Faculty Member, Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University

Ahmed is an assistant professor within the Department of Medicine at Rutgers Robert Wood Johnson Medical School.

The scientists stated that future research must extend this method by examining the complete set of genes in patients with cardiovascular disease. This might disclose significant biomarkers and risk factors linked to the vulnerability of cardiovascular disease.

The coauthors of the study include Vignesh Venkat, Habiba Abdelhalim, William DeGroat of IFH, and Saman Zeeshan of Rutgers Cancer Institute of New Jersey.

Journal Reference:

Venkat, V., et al. (2023) Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine. Genomics.

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