Artificial intelligence neural network based on intelligent diagnosis

Abstract

The medical model that uses artificial intelligence technology to assist diagnosis and treatment is called smart medicine. It can learn the medical knowledge of experts, and can simulate the thinking and reasoning of doctors to give patients a reliable diagnosis and treatment plan. The purpose of this article is to help young doctors with uneven distribution of medical resources and insufficient experience by exploring the application of artificial intelligence neural networks in the intelligent diagnosis of diabetes. This paper proposes to use BP neural network and probabilistic neural network to model diabetes diagnosis. First, the number of hidden layer units of BP network is selected according to the input feature vector. The training effect is best when the number of hidden layer units is 12, and the diagnostic accuracy rate is 91.7% through experiments. Then built a PNN network model, using 75% of the data for training, 25% of the data for testing, and testing the effectiveness of the network model. The diagnostic accuracy was 97.9%. Finally, the accuracy of the 20 neural network models was tested 20 times. After a comparative analysis, it is concluded that the PNN network model is better than the BP neural network model in terms of performance and accuracy. Compared with the traditional diagnosis process, the neural network-based diagnosis model can effectively save doctors time and improve diagnosis efficiency.