Application of Back Propagation Neural Network Model in Prediction and Diagnosis of Osteoporosis

Abstract

The classification model of osteoporosis was established, the diagnostic index system of osteoporosis was analyzed, and the BP neural network system was designed. The data of 1261 people from 402 nuclear families were collected, including age, gender and medical history. The actual data were sampled and tested by using the computational function of ANN, so that the model error could be controlled within a set range. The results of neural network analysis suggested that among the 12 factors influencing bone mineral density, gender had the most obvious effect, followed by height, weight and age. The polymorphism of estrogen receptor PvuII had the greatest impact on bone mineral density (ER PvuII) of all candidate genes, and the factor that had the most serious effect was BGP gene. A large number of BP network training test results were within the expected error range, and the same conclusion was drawn using the same sample and traditional statistical methods.Therisk factors application has certain feasibility and accuracy.