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Research on Disease Prediction Method Based on R-Lookahead-LSTM
Cardiovascular disease is one of the most serious diseases that threaten human health in the world today. Therefore, establishing a high-quality disease prediction model is of great significance for the prevention and treatment of cardiovascular disease. In the feature selection stage, three new str...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020897/ https://www.ncbi.nlm.nih.gov/pubmed/35463275 http://dx.doi.org/10.1155/2022/8431912 |
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author | Chen, Hailong Du, Mei Zhang, Yingyu Yang, Chang |
author_facet | Chen, Hailong Du, Mei Zhang, Yingyu Yang, Chang |
author_sort | Chen, Hailong |
collection | PubMed |
description | Cardiovascular disease is one of the most serious diseases that threaten human health in the world today. Therefore, establishing a high-quality disease prediction model is of great significance for the prevention and treatment of cardiovascular disease. In the feature selection stage, three new strong feature vectors are constructed based on the background of disease prediction and added to the original data set, and the relationship between the feature vectors is analyzed by using the correlation coefficient map. At the same time, a random forest algorithm is introduced for feature selection, and the importance ranking of features is obtained. In order to further improve the prediction effect of the model, a cardiovascular disease prediction model based on R-Lookahead-LSTM is proposed. The model based on the stochastic gradient descent algorithm of the fast weight part of the Lookahead algorithm is optimized and improved to the Rectified Adam algorithm; the Tanh activation function is further improved to the Softsign activation function to promote model convergence; and the R-Lookahead algorithm is used to further optimize the long-term memory network model. Therefore, the long- and short-term memory network model can be better improved so that the model tends to be stable as soon as possible, and it is applied to the cardiovascular disease prediction model. |
format | Online Article Text |
id | pubmed-9020897 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90208972022-04-21 Research on Disease Prediction Method Based on R-Lookahead-LSTM Chen, Hailong Du, Mei Zhang, Yingyu Yang, Chang Comput Intell Neurosci Research Article Cardiovascular disease is one of the most serious diseases that threaten human health in the world today. Therefore, establishing a high-quality disease prediction model is of great significance for the prevention and treatment of cardiovascular disease. In the feature selection stage, three new strong feature vectors are constructed based on the background of disease prediction and added to the original data set, and the relationship between the feature vectors is analyzed by using the correlation coefficient map. At the same time, a random forest algorithm is introduced for feature selection, and the importance ranking of features is obtained. In order to further improve the prediction effect of the model, a cardiovascular disease prediction model based on R-Lookahead-LSTM is proposed. The model based on the stochastic gradient descent algorithm of the fast weight part of the Lookahead algorithm is optimized and improved to the Rectified Adam algorithm; the Tanh activation function is further improved to the Softsign activation function to promote model convergence; and the R-Lookahead algorithm is used to further optimize the long-term memory network model. Therefore, the long- and short-term memory network model can be better improved so that the model tends to be stable as soon as possible, and it is applied to the cardiovascular disease prediction model. Hindawi 2022-04-13 /pmc/articles/PMC9020897/ /pubmed/35463275 http://dx.doi.org/10.1155/2022/8431912 Text en Copyright © 2022 Hailong Chen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Chen, Hailong Du, Mei Zhang, Yingyu Yang, Chang Research on Disease Prediction Method Based on R-Lookahead-LSTM |
title | Research on Disease Prediction Method Based on R-Lookahead-LSTM |
title_full | Research on Disease Prediction Method Based on R-Lookahead-LSTM |
title_fullStr | Research on Disease Prediction Method Based on R-Lookahead-LSTM |
title_full_unstemmed | Research on Disease Prediction Method Based on R-Lookahead-LSTM |
title_short | Research on Disease Prediction Method Based on R-Lookahead-LSTM |
title_sort | research on disease prediction method based on r-lookahead-lstm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020897/ https://www.ncbi.nlm.nih.gov/pubmed/35463275 http://dx.doi.org/10.1155/2022/8431912 |
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