Key result
Improved deep belief network predicts cardiovascular disease with ~91% accuracy across validation datasets.
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Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN) is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart) and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.
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Lu et al. (2018) studied Cardiovascular disease. Improved deep belief network (DBN) prediction model vs. Traditional models and shallow neural networks was evaluated on Mean prediction accuracy for cardiovascular disease. An improved deep belief network (DBN) model achieved mean cardiovascular disease prediction accuracies of 91.26% and 89.78% across two UCI datasets.
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