Study on the nursing of patients with early dementia based on a hierarchical genetic algorithm (GA‐RBF) neural network system

Abstract Objective In order to improve the efficiency and prediction accuracy of BP neural network for disease diagnosis, a genetic algorithm was proposed to optimize BP neural network for the diagnosis of Alzheimer's disease to verify the more practical and dementia diagnosis. Methods: Taking the electronic medical record data mining in hospital as an example, a predictive model was established for the diagnosis of Alzheimer's disease. The method firstly uses the genetic algorithm's search optimization technology to carry out feature reduction, and then uses the reduced feature as the input variable of BP neural network to train and construct the BP neural network model. The simulation experiment was carried out on the MATLAB software platform. Results: Compared with single BP neural network, genetic algorithm optimization BP neural network can reduce the training time of the model and improve the prediction accuracy. It is a feasible auxiliary diagnosis method for Alzheimer's disease. Conclusion: The application of hierarchical genetic algorithm GA‐RBF neural network system and early prediction of senile dementia and cognitive status can help to accurately predict the cognitive status of the elderly, prevent it in advance, and improve the quality of care for patients with Alzheimer's disease clinical promotion.