Research On Cerebrovascular Disease Prediction System Based On The Long Short Term Memory Neural Network

Cerebrovascular disease has the characteristics of high incidence,high disability rate,high recurrence rate and high mortality.At present,the number of patients with cerebrovascular disease and mortality have been ranked first in all countries.After the onset of cerebrovascular disease,the possibility of recurrence is extremely high,and the risk of recurrence is 9 times that of the average individual,resulting in a significant increase in the recurrence rate,which also emphasizes the importance of secondary prevention of cerebrovascular disease.Therefore,the prevention of cerebrovascular diseases has become the focus of current development in the medical field.Constructing a disease prediction model is an important means of prevention of cerebrovascular diseases.In this context,in order to further improve the medical level and prevent cerebrovascular diseases,a predictive index system for cerebrovascular diseases was constructed in this paper,and the applicability of LSTM neural network to medical data with time series characteristics was established.The disease prediction model of LSTM neural network was constructed to predict the risk of recurrence of cerebrovascular disease.The main research contents of this paper are as follows:(1)Build a new diagnosis and treatment model and predictive indicator system.Analyzed the current diagnosis and treatment mode of cerebrovascular diseases and the limitations.A new diagnosis and treatment model based on disease prediction is proposed.Based on the risk scoring scale such as Essen,this paper proposes a forward greedy feature selection algorithm based on domain rough set,and establishes a predictive index system for cerebrovascular diseases including six first-level indicators and 18 second-level indicators.(2)Construct a predictive model of cerebrovascular disease.Based on the time series characteristics of collected cerebrovascular disease data,a predictive model of cerebrovascular disease was established based on LSTM neural network.The training parameters were used to adjust the model parameters,and the Adam algorithm was used to optimize the model,and finally the model was constructed.The test data is used to evaluate the performance of the model,and the prediction results are compared with the results of the support vector machine prediction model to verify the validity of the model.(3)Design and implementation of a cerebrovascular disease prediction system.Through the analysis of system functions and non-functional requirements and design,the development of major functional modules such as disease prediction is completed.Propose application recommendations for the prediction system,and the application of LSTM prediction model in cerebrovascular disease prediction system is realized.In this paper,the field rough set theory is used to complete the screening of predictive indicators,and the LSTM neural network is applied to the risk prediction of cerebrovascular diseases.The disease recurrence risk prediction and personalized intervention are realized,and the recurrence rate and mortality rate of the disease are reduced.It provides an effective method for predicting cerebrovascular diseases.