Kavli Affiliate: Ting Xu
| First 5 Authors: Yuxiang Hu, Jinxin Hu, Ting Xu, Bo Zhang, Jiajie Yuan
| Summary:
This project intends to study a cardiovascular disease risk early warning
model based on one-dimensional convolutional neural networks. First, the
missing values of 13 physiological and symptom indicators such as patient age,
blood glucose, cholesterol, and chest pain were filled and Z-score was
standardized. The convolutional neural network is converted into a 2D matrix,
the convolution function of 1,3, and 5 is used for the first-order convolution
operation, and the Max Pooling algorithm is adopted for dimension reduction.
Set the learning rate and output rate. It is optimized by the Adam algorithm.
The result of classification is output by a soft classifier. This study was
conducted based on Statlog in the UCI database and heart disease database
respectively. The empirical data indicate that the forecasting precision of
this technique has been enhanced by 11.2%, relative to conventional approaches,
while there is a significant improvement in the logarithmic curve fitting. The
efficacy and applicability of the novel approach are corroborated through the
examination employing a one-dimensional convolutional neural network.
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