Diabetes Prediction Model Based On GSA-LA Optimized GRU Neural Network

With the continuous improvement of people’s lives,the problem of population aging is also aggravated,and chronic diseases of aging such as diabetes have gradually become an important threat to health.The study found that the establishment of a reasonable diabetes prediction model,through early detection of diabetes and targeted treatment measures,can effectively control diabetes-related complications.However,the current research on diabetes prediction has the problem of low prediction accuracy due to many characteristic factors.To this end,this paper takes advantage of deep learning technology in multi-dimensional data processing and introduces it into diabetes model prediction research.Through the analysis and feature screening of diabetes case data,combined with Learning Automata(LA),gravitational search Based on the advantages of Gravity Search Algorithm(GSA)and Gated Recurrent Unit(GRU)neural network,a novel diabetes prediction model was proposed,and corresponding simulation experiments were designed.The research contents include the following aspects:(1)Research on preprocessing and feature selection of diabetes data.Select the original diabetes data set published by Tianchi Precision Medicine Competition as the research object,analyze the laws of data existence,carry out research on data preprocessing technology and feature selection technology,and complete the preprocessing and feature screening of diabetes data.(2)Research on gravitational search algorithm for neural network parameter optimization.When using neural network to predict diabetes,because the gradient descent method is usually used to update the weights,the algorithm is easy to fall into the problem of local minimum.To this end,a neural network parameter optimization algorithm based on LAGSA is proposed.Using LA to automatically adjust the gravitational constant G(t)in GSA,the optimization accuracy of GSA is improved,and the search accuracy of high-dimensional space optimization is effectively solved.lower problem.(3)Deep learning optimization research for diabetes prediction.Combined with the advantages of LAGSA algorithm being simple and easy to implement,less parameter adjustment,and the characteristics of GRU neural network,a diabetes prediction model based on LAGSA optimized GRU neural network(LA-GSA-GRU).The GRU neural network parameters are optimized by LAGSA,which improves the prediction accuracy of the model.Finally,the LA-GSA-GRU prediction model validation experiment is designed.First,the LAGSA algorithm and other methods are tested on 12 benchmark functions,and the results show that the LAGSA algorithm is more effective in finding the optimal solution and has higher search accuracy;LA-GSA-GRU prediction model is evaluated,and compared with other algorithms,the results prove that the model has obvious advantages and further improves the prediction accuracy.