Heart attacks can be predicted by AI more accurately than doctors

This machine learning algorithm beats ACC/AHA guidelines by 7.6 percent

An estimated 20 million people die each year as a result of cardiovascular disease. Luckily, a team of researchers in the University of Nottingham in the united kingdom have developed a machine learning algorithm that will predict your chance of having any doctor in addition to a heart attack or stroke.

The American College of Cardiology/American Heart Association (ACC/AHA) has developed a succession of guidelines for estimating a patient's cardiovascular risk which is based on eight factors including age, cholesterol level and blood pressure. On average, this system right guesses an individual 's risk in a speed of 72.8 percent.

That is pretty accurate but Stephen Weng and his team set going to help it become better. They constructed four computer learning algorithms, then fed them in the United Kingdom. patients information from 378,256 The systems used around 295,000 records to create their internal predictive models. They used the remaining records to test and refine them. The neural network algorithm examined highest, surpassing the present guidelines by 7.6 percent while increasing 1.6 percent fewer false alarms.

Out of the 83,000 patient set of test records, this system could have conserved 355 extra lives. The AI systems identified a number of risk factors and predictors not covered in the existing guidelines, like acute mental illness and the consumption of oral corticosteroids. "That is the reality of the human body. What computer science enables us to do is always to research those organizations."

Source: Science

AI American College of Cardiology American Heart Association AmericanCollegeofCardiology AmericanHeartAssociation ArtificialIntelligence cardiovascular guidelines health Heart Attack medicine modelling predictive science United Kingdom University of Nottingham