Abstract: BP neural network is one of the most frequently used neural networks in deep learning research. In this paper, an improved genetic algorithm （IGABP） is proposed to optimize the initial structure of BP neural network. The genetic algorithm is easy to fall into local optimal solution, which affects its own optimization ability, so the genetic algorithm is improved, and finally the prediction model of diabetes complications is constructed to predict the occurrence of diabetes complications. The selection operator of genetic algorithm is improved, and the crossover and mutation probability formula of adaptive genetic algorithm is improved also. By building a prediction model, the improved IGABP is compared with BP, GABP and AGABP. The simulation results show that the prediction accuracy of IGABP is significantly better than that of BP, GABP and AGABP, and the convergence speed of the network is accelerated.

Key words: genetic algorithm, BP neural network, self adaptation, diabetes prediction, data preprocessing