A modal of the heart disease severity diagnosis and evaluation based on rough set theory and BP neural network

A modal Based on rough set theory and BP neural network for the heart disease severity diagnosis and evaluation is proposed. According to the heart disease symptoms data quantized by Konhonen neural network, a decision table is created and reduced using the rough set theory. After reducing the decision table, the symptoms data are the input of the BP neural network, and the diagnosis data are the output. The practical application shows that using rough sets theory and BP neural network can enhance effectively accuracy, speed of diagnosis and reduce some checking items and checking cost.