Deconstruction of Risk Prediction of Ischemic Cardiovascular and Cerebrovascular Diseases Based on Deep Learning

Author:

Xu Yan1,Meng Lingwei2ORCID

Affiliation:

1. Unit 36, Department of Neurology, Daqing Oilfield General Hospital, Daqing 163714, Heilongjiang, China

2. Daqing Oilfield General Hospital, Daqing 163714, Heilongjiang, China

Abstract

Over the years, with the widespread use of computer technology and the dramatic increase in electronic medical data, data-driven approaches to medical data analysis have emerged. However, the analysis of medical data remains challenging due to the mixed nature of the data, the incompleteness of many records, and the high level of noise. This paper proposes an improved neural network DBN-LSTM that combines a deep belief network (DBN) with a long short-term memory (LSTM) network. The subset of feature attributes processed by CFS-EGA is used for training, and the optimal selection test of the number of hidden layers is performed on the upper DBN in the process of training DBN-LSTM. At the same time, the validation set is combined to determine the hyperparameters of the LSTM. Construct the DNN, CNN, and long short-term memory (LSTM) network for comparative analysis with DBN-LSTM. Use the classification method to compare the average of the final results of the two experiments. The results show that the prediction accuracy of DBN-LSTM for cardiovascular and cerebrovascular diseases reaches 95.61%, which is higher than the three traditional neural networks.

Publisher

Hindawi Limited

Subject

Radiology, Nuclear Medicine and imaging

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Prediction of Stroke Risk with LSTM Networks and Patient Health Records;2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI);2023-12-29

2. Retracted: Deconstruction of Risk Prediction of Ischemic Cardiovascular and Cerebrovascular Diseases Based on Deep Learning;Contrast Media & Molecular Imaging;2023-07-19

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