An anonymous reader quotes a report from Digital Trends: Scientists from the University of Nottingham in the United Kingdom have managed to develop an algorithm that outperforms medical doctors when it comes to predicting heart attacks. 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. In employing computer science, Stephen Weng, an epidemiologist at the University of Nottingham, 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.