This AI system predicts heart attack accuracy of 72.8%
[Netease smart news April 17 news] Every year about 20 million people die of cardiovascular disease. Fortunately, researchers at the University of Nottingham, UK, have developed a machine-learning algorithm that can predict the likelihood of you having a heart attack or stroke that is even more accurate than human doctors.
The American Heart Association and the American Heart Association have developed a series of guidelines for assessing the risk of cardiovascular disease in patients, based primarily on eight factors, including age, cholesterol level, and blood pressure. In general, the accuracy of this system prediction can reach 72.8%. This is quite accurate, but Stephen Weng and his team think it can make it better.
They established four computer learning algorithms and then provided them with data from 378,256 patients in the United Kingdom. For the first time, the system uses approximately 295,000 records to generate internal prediction models, and then uses the remaining records to test and refine them. The results of this algorithm are significantly better than those of the American Heart Association and the American Heart Association, and their accuracy varies from 74.5% to 76.4%.
The accuracy of the detection results of the neural network algorithm is the highest, which is 7.6% higher than the existing guidance standard, and false alarms are also reduced by 1.6%. In the test record of 83,000 patients, this system may save 355 more. Interestingly, artificial intelligence systems do not recognize many risk factors and predictors, such as severe mental illness and oral corticosteroids.
Weng said: "There are many interactions in biological systems. This is the reality of the human body. What computer science can do is explore the connections between them."
(English source /engadget, compiler / machine Xiaoyi, proofreading / small)
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