"Machine learning approaches offer the exciting prospect of achieving improved and more individualized CVD risk assessment", the team continued. However, it seems certain types are already smarter than what was originally predicted. Luckily, a team of researchers from the University of Nottingham in the United Kingdom have developed a machine-learning algorithm that can predict your likelihood of having a heart attack or stroke as well as any doctor, Engadget said.
Since this is new technology, it hasn't been implemented by doctors worldwide yet.
There are plenty of use cases for artificial intelligence in the healthcare sector. An epidemiologist from the University of Nottingham in the UK, Stephen Weng, said, "What computer science allows us to do is to explore those associations". This is where machine-learning solutions can make a big impact, especially when it comes to determining severe medical conditions such as a heart attack or a stroke.
The Nottingham researchers developed four different computer programs, each using a different algorithm. To do so, they first analyzed patterns that occur around the time that cardiovascular events such as heart attacks occur. Moreover, this AI solution can do so at roughly a 76% accuracy.
Weng and his team compared the use of the regular ACC/AHA guidelines with four machine-learning algorithms - random forest, logistic regression, gradient boosting, and neural networks -all of which have picked up the various risk factors involved in heart diseases. The neural network algorithm tested highest, beating the existing guidelines by 7.6 percent while raising 1.6 percent fewer false alarms. Once that step was completed, the algorithms used the rest of the data to test and refine said models. The algorithms were trained on real patient records and developed criteria that outperformed the current guidelines set by the American Heart Association.
We have been talking about benefits of machine-learning investments for quite some time. Computers and doctors working together perform better than doctors alone.
Standard models, he explained, tend to "oversimplify" cardiovascular disease (CVD); eight core baseline variables-gender, age, smoking status, systolic blood pressure, blood pressure treatment, total cholesterol, HDL cholesterol, diabetes-don't automatically foretell a heart attack. More than 295,000 served to train the machines, while the remaining 82,989 subjects provided validation.