New article: Artificial intelligence defeat doctors at predicting heart attacks read more at here http://www.spinonews.com/index.php/science/item/3338-artificial-intelligence-defeats-doctor-at-predicting-heart-attacks

Now technology has been widely spread in the stream of medical and health. As we know the system with technology used in testing the human health can give out good accurate results. So mostly willing to maintain the adaptability between technology and medical science. Doctors maintain different types of tools in examining and predicting the health of a patient. Sometimes doctors with their experienced knowledge couldn’t predict the illness with the complexity present in the human body.

Scientists have computers with great technology that can be accurate in predicting the Heart attacks with the capable of teaching themselves and it is better than the standard medical guidelines. The diseases with in the heart is difficult to predict and this lead to several millions of people’s death. Using the system and technology we can save many people related to heart diseases and increasing the prediction rate. The effects of cardiovascular disease, including heart attacks, strokes, blocked arteries, and other circulatory system malfunctions. Those are based on eight risk factors including age, cholesterol level, and blood pressure that physicians effectively add up. Nearly 20 million people die annually

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The scientists have compared the general standard medical guidelines with the four learning algorithms: random forest, logistic regression, gradient boosting, and neural networks. All four techniques analyze the data in order to come up with predictive tools without any human instruction. In this case, the data came from the electronic medical records of 378,256 patients in the United Kingdom. The goal was to find patterns in the records that were associated with cardiovascular events.

Artificial intelligence (AI) algorithms had to train themselves. They used about 78% of the data some 295,267 records to search for patterns and build their own internal guidelines. They then tested themselves on the remaining records. Using record data available in 2005, they predicted which patients would get their first cardiovascular treatment over the next 10 years, and checked the guesses against the 2015 records. Unlike the ACC/AHA guidelines, the machine-learning methods were allowed to take into account 22 more data points, including ethnicity, arthritis, and kidney disease.

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All four AI methods performed significantly better than the ACC/AHA guidelines. Using a statistic called AUC in which a score of 1.0 signifies 100% accuracy the ACC/AHA guidelines hit 0.728. The four new methods ranged from 0.745 to 0.764. The best one neural networks correctly predicted 7.6% more events than the ACC/AHA method, and it raised 1.6% fewer false alarms. In the test sample of about 83,000 records that amounts to 355 additional patients whose lives could have been saved. That’s because prediction often leads to prevention through cholesterol-lowering medication or changes in diet.