Sample expansion-oriented LDBN heart disease risk forecast model
Abstract
The risk forecast model of heart disease provides an important method for predicting the incidence of heart disease. This paper proposes a sample expansion-oriented LDBN heart disease risk forecast model, in order to solve the following problems: the difficulty of related medical data acquisition in heart disease diagnosis, less samples, the slow convergence speeds and easily caught in the local optimum of existing risk forecast models. Starting from LSTM (Long Short Term Memory) that forecast the information of the next moment with extract knowledge from existing information and combined with the current moment input information or the previous layer output information of DNN (Deep Neural Networks), the model simulates the information of patients with heart disease and adopts the corresponding strategies to expand medical data about heart disease. Then, we build a risk forecast model based on LDBN (Long Deep Belief Networks) for more accurate prediction combing with DBN (Deep Belief Network) and the improved recurrent neural Network (Long Short Term Memory) and use Heart Disease Databases of UCI to verify the accuracy of the model. Comparing with BP and DBN model, the accuracy of the proposed model is respectively increased by 2.9.
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The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima.
“Furthermore, the regular learning process in ANFIS is dependent on gradient-based learning which has a tendency to become trapped in local minima [14].”
From A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS (2020) · cites this paper as [14]
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Prediction of Cardiovascular Disease using Artificial Neural Network
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Analysis of Various Smart Healthcare Systems for Heart Disease Prediction
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- Authors
- Wang, Z., Chen, J., Zheng, Z., & Wang, J.
- Affiliations
- Zhengzhou University (CN)
- Venue
- 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1327-1332
- OpenAlex
- W2889179825
- Cited by
- 7
- References
- 6
- Citations by year
- 2024: 3 · 2023: 1 · 2021: 1 · 2020: 2
- FWCI
- 0.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Wireless Communications and Mobile Computing Conference (IWCMC) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- heart disease risk prediction, long short-term memory, deep belief network, sample expansion, medical data acquisition, small sample learning, recurrent neural networks
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