Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction

Zhang, Zhichang; Qiu, Yanlong; Yang, Xiaoli; Zhang, Minyu

doi:10.1186/s12911-020-1118-z

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…Reference [ 14 ] proposed an online medical decision support system for predicting chronic kidney disease. Reference [ 15 ] proposed an enhanced feature-level deep convolutional neural network model. The researchers went from initially using a machine algorithm to integrating statistical models and mathematical models into machine learning models, which further improved the prediction effect of the model.…”

Section: Introductionmentioning

confidence: 99%

Research on Disease Prediction Method Based on R-Lookahead-LSTM

Chen

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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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.

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Section: Introductionmentioning

confidence: 99%

Research on Disease Prediction Method Based on R-Lookahead-LSTM

Chen

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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“…When comparing the results it is shown that our proposed system effectively outperforms in terms of the evaluation performance metrics. Comparative analysis of the QoS parameters of the existing methods and the proposed system[35]…”

mentioning

confidence: 99%

Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier

Kalaivani¹,

Maheswari

Venkatesh

2022

Network: Computation in Neural Systems

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Background: Cardiac Disease is the predominant cause of global death mainly due to its hidden symptoms and late diagnosis. People with CVD along with other diseases like hypertension, hyperlipidemia require very early detection for appropriate treatment. Hence this research proposed a hybrid technique for heart disease diagnosis. OBJECTIVE The main contribution of the study is to overcome the existing limitations of Antlion, Crow search, and improved genetic algorithm and to hybridize the algorithm for the effective feature selection thereby improving the classification performance of the LSTM classifier. The motivation of this research is to improve the feature selection method with the optimization of features for effective heart disease prediction. Methodology: The proposed architecture uses the Ant lion algorithm with the effective determination of the elite position. The crow search Algorithm utilizes the phenomenon of position and memory of each crow for the evaluation of objective function. The inputs were processed by the improved genetic algorithm for effective feature selection. Now the hybridized proposed system successfully extracts the optimized features. These features are classified by the LSTM classifier. Results:The performance analysis was performed with two datasets. The dataset 1 had been used for the determination of the efficiency of the proposed system and the dataset 2 is utilized for the estimation of the proposed system followed by a detailed comparative analysis with the existing system. Further cross-validation of the sample with a varied range of testing percentage has also been accomplished. Apart from the intercombination performance of feature selection among the three utilized algorithms was also compared. From this comparative analysis, the proposed method had the highest accuracy of 99.7% compared to existing methods.

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Cardiovascular disease prognosis and severity analysis using hybrid heuristic methods

Kumar¹,

Ramana²

2020

Multimed Tools Appl

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Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction

Cited by 9 publications

References 17 publications

Research on Disease Prediction Method Based on R-Lookahead-LSTM

Research on Disease Prediction Method Based on R-Lookahead-LSTM

Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier

Cardiovascular disease prognosis and severity analysis using hybrid heuristic methods

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