Dr Stephen Weng, from the university's NIHR School for Primary Care Research, 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. These AI algorithms have the potential to help save more lives". Professor Jon Garibaldi and Dr Jenna Reps, of the Advanced Data Analysis Centre in the School of Computer Science, said: "We were curious to find out how four modern machine learning algorithms would perform given the large data set of 378,256 patients from nearly 700 UK GP practices.

Computers that can teach themselves from routine clinical data are potentially better at predicting cardiovascular risk than current standard medical risk models, according to new research at the University of Nottingham. The team of primary care researchers and computer scientists compared a set of standard guidelines from the American College of Cardiology (ACC) with four'machine-learning' algorithms – these analyse large amounts of data and self-learn patterns within the data to make predictions on future events – in this case, a patient's future risk having of heart disease or a stroke. The results, published in the online journal PLOS ONE, showed that 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'. Dr Stephen Weng, from the university's NIHR School for Primary Care Research, 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.

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