Abstract
Aiming at the characteristics of high recurrence rate of cerebrovascular disease and the low prediction accuracy of traditional methods, a prediction model of recurrent risk of cerebrovascular disease based on long-term and short-term memory (LSTM) neural network was proposed. The predictive index of cerebrovascular disease was screened by the forward greedy attribute reduction algorithm based on the domain rough set theory. The long-short memory neural network was used to train and predict the cerebrovascular disease dataset. Through the model simulation, the results show that the proposed method has higher accuracy and better prediction performance than the support vector machine (SVM) method.
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Qin, Q., Yao, C., Jiang, Y. (2019). Research on Cerebrovascular Disease Prediction Model Based on the Long Short Term Memory Neural Network. In: Chen, H., Zeng, D., Yan, X., Xing, C. (eds) Smart Health. ICSH 2019. Lecture Notes in Computer Science(), vol 11924. Springer, Cham. https://doi.org/10.1007/978-3-030-34482-5_22
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DOI: https://doi.org/10.1007/978-3-030-34482-5_22
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