Nottingham scientists discover artificial intelligence which predicts future heart disease and strokes

Researchers have found that self-teaching 'artificially intelligent' tools are significantly more accurate in predicting cardiovascular disease than established medical models.

Nottingham scientists have discovered artificial intelligence on computers that is better at predicting heart disease and strokes than humans.

A team of primary care researchers and computer scientists at the University of Nottingham compared a set of standard guidelines from the American College of Cardiology (ACC) with four ‘machine-learning’ algorithms.

The algorithms analyse large amounts of data and self-learn patterns to make predictions on future events – which in this case was a patient’s future risk of having heart disease or a stroke.

The results showed the self-teaching ‘artificially intelligent’ tools were significantly more accurate in predicting cardiovascular disease than the established algorithm.

In computer science, the AI algorithms that were used are called ‘random forest’, ‘logistic regression’, ‘gradient boosting’ and ‘neural networks’.

What is cardiovascular disease?

Cardiovascular disease (CVD) is a general term for conditions affecting the heart or blood vessels.

It’s usually associated with a build-up of fatty deposits inside the arteries – known as atherosclerosis – and an increased risk of blood clots.

It can also be associated with damage to arteries in organs such as the brain, heart, kidneys and eyes.

CDV is one of the main causes of death and disability in the UK, but it can often largely be prevented with a healthy lifestyle.

Dr Stephen Weng, from the university’s NIHR School for Primary Care Research, says these AI algorithms have the potential to help save more lives.

He said: “Cardiovascular disease is the leading cause of illness and death worldwide.

“Our study shows that artificial intelligence could significantly help in the fight against it by improving the number of patients accurately identified as being at high risk and allowing for early intervention by doctors to prevent serious events like cardiac arrest and stroke.

“Current standard prediction models like the ACC are based on eight risk factors including age, cholesterol level and blood pressure but are too simplistic to account for other factors like medications, multiple disease conditions, and other non-traditional biomarkers.”

The study concludes that the improved predictions offered by self-teaching algorithms are better at predicting the absolute number of cardiovascular disease cases correctly, while successfully excluding non-cases.

The research team believes AI has a crucial role to play in healthcare tools of the future which will deliver medicine suitable on a case by case basis.

The team says improvement in predictive accuracy could be further explored using machine learning with other large clinical datasets in other populations and in predicting other disease outcomes.

However future investigation of machine-learning applications in clinical practice is needed before any real life clinical application of this new technology is rolled out.


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