Prediction Model of Diabetes Complications Based on Genetic Engineering Improved Genetic Algorithm Optimized BP Neural Network

Abstract

Nowadays, with the rapid development of science and technology, people’s living standards have been greatly improved, and many chronic diseases have also been brought, including diabetes. The occurrence of diabetes not only poses a serious threat to human body, but also poses a threat to human life with its development. BP (Back Propagation) neural network model can well solve the logic regression problem of single factor and multiple factors, and also better solve the collinearity problem of multiple factors. BP neural network optimized based on improved genetic algorithm can reflect the influence mode and degree of various factors, and can be predicted from the perspective of patients’ diet, exercise, and doctors’ application of insulin. In this paper, the patients in a hospital were taken as the research object, and the BP neural network method was used to analyze the causes of the disease. The prediction model was used to screen out the high-risk groups of diabetes patients and reduce their incidence rate. Secondly, according to the collected data, the relationship between diabetes related complications was analyzed in depth by using genetic engineering technology, thus providing a theoretical basis for the prevention and treatment of diabetes and its complications. The prediction accuracy of BP neural network optimized by genetic algorithm can reach 94.1%. In the high-risk group of diabetes, taking appropriate diet and behavioral measures can reduce the probability of diabetes. The prediction scheme of diabetes complications proposed in this paper is simple and its cost is low, which can greatly reduce the cost of prevention and treatment of diabetes and the probability of diabetes.