A Hybrid Neural Network based on Particle Swarm Optimization for Predicting the Diabetes

In recent years, more and more studies have applied hybrid models in order to improve the performance of traditional neural networks. By combining particle swarm optimization and neural network, this research proposes a new hybrid neural prediction algorithm named as PSONN. The algorithm was applied to Pima Indians Diabetes Database and compared with eight other algorithms including Logistic regression, Ridge regression, Lasso regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Random Forest, Gradient Boosting Machine, and Adam (a neural network algorithm). The findings indicated that the proposed algorithm had higher accuracy and stability, but it took more time to execute. It is suggested that, future research could apply parallelization technology for reducing execution time.