As it stands, around 20 million people fall victim to cardiovascular disease, which includes heart attacks, strokes, and blocked arteries. Today, doctors depend on guidelines similar to those of the American College of Cardiology/American Heart Association (ACC/AHA) in order to predict individuals’ risks. These guidelines include factors like age, cholesterol level, and blood pressure.
Unfortunately, that’s often insufficient. “There’s a lot of interaction in biological systems,” Stephen Weng, an epidemiologist at the University of Nottingham, told Science Magazine. And some of them make less sense than others. “That’s the reality of the human body,” Weng continued. “What computer science allows us to do is to explore those associations.”
In employing computer science, Weng took the ACC/AHA guidelines and compared them to four machine-learning algorithms: random forest, logistic regression, gradient boosting, and neural networks. The artificially intelligent algorithms began to train themselves using existing data to look for patterns and create their own “rules.” Then, they began testing these guidelines against other records. And as it turns out, all four of these methods “performed significantly better than the ACC/AHA guidelines,” Science reports.
The most successful algorithm, the neural network, actually was correct 7.6 percent more often than the ACC/AHA method, and resulted in 1.6 percent fewer false positives. That means that in a sample size of around 83,000 patient records, 355 additional lives could have been saved.
“I can’t stress enough how important it is,” Elsie Ross, a vascular surgeon at Stanford University in Palo Alto, California, who was not involved with the work, told Science, “and how much I really hope that doctors start to embrace the use of artificial intelligence to assist us in care of patients.”
