Sep. 16, 2010 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 fa