Evaluating risk of cardiovascular disease, heart failure, high blood pressure, or stroke may be as easy as plugging data into a computer program, a new report suggests. This method includes using a software program to analyze data to notice correlations within a neural network.
Researchers have created a diagnostic system to help predict risk of cardiovascular disease using genetic algorithms, fuzzy logic, and neural networks. The new model shows an accuracy rate of about 90 percent when determining risk in patients.
Cardiovascular disease is a broad term related to issues within the heart or blood vessels. Common examples include high blood pressure, heart failure, congenital heart disease, and coronary heart disease. In 2009, the World Health Organization estimated nearly 20 million deaths each year result from cardiovascular disease, and the number increases year by year.
Discovering potential issues early is the key to reducing death rates from cardiovascular disease, experts explain. Using a proactive approach instead of reactive is one of the important benefits from this new system.
The research team explains how constant improvements are being made to the system, and implementing additional rules will create an even more accurate model for risk assessment.