Heart Disease and Stroke Risk Revealed By Fuzzy Thinking
Medical News Today
A new approach to evaluating a person's risk of cardiovascular disease, stroke, high blood pressure, or heart failure is reported this month in the International Journal of Data Mining, Modelling and Management. The technique uses fuzzy logic to teach a neural network computer program to analyze patient data and spot correlations that can be translated into a risk factor for an individual.
Khanna Nehemiah of the Anna University Chennai, India, and colleagues have developed a medical diagnostic system for predicting the severity of cardiovascular disease based on combining the fuzzy logic, neural networks and genetic algorithms. The resulting statistical model improves on previous attempts and is accurate 9 times in 10 in determining patient risk.
Cardiovascular disease (CVD) refers to disorders of the heart or blood vessels and includes coronary heart disease, cerebrovascular disease, raised blood pressure, peripheral artery disease, rheumatic heart disease, congenital heart disease and heart failure. The World Health Organization in 2009 estimated that almost 20 million deaths occur annually from cardiovascular disease and that by 2030 that figure could rise to almost 24 million.
National Stroke Association’s mission is to reduce the incidence and impact of stroke by developing compelling education and programs focused on prevention, treatment, rehabilitation and support for all impacted by stroke.