Aside from the almost-instantaneous warning signs--chest pain, shortness of breath, nausea--heart attacks are notoriously hard to predict. Standard models tend to "oversimplify" cardiovascular disease, reducing risk to eight core baseline variables.
These factors don't automatically foretell a heart attack. Machine learning offers an alternative approach, exploiting big data to minimize human error. Using four computer-learning algorithms, nearly 25,000 fatal or non-fatal cardiovascular events were documented over the study's 10-year period, about 75 percent of which were accurately predicted by the algorithms.
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