Author: Liu, Yuliang; Zhang, Quan; Zhao, Geng; Liu, Guohua; Liu, Zhiang
Title: Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically Document date: 2020_3_11
ID: 1r4gm2d4_29
Snippet: We used 500 data to train the deep learning model, and the remaining 100 data were used to test the final performance of the model. The physiological characteristic sequence was fed to train the model. The training data mentioned above were divided into two parts: (1) Training-Sample: 90% of the training data was used to optimize the global parameters of the model and (2) Hyperparameters-Sample: the remaining 10% of the training data was used to .....
Document: We used 500 data to train the deep learning model, and the remaining 100 data were used to test the final performance of the model. The physiological characteristic sequence was fed to train the model. The training data mentioned above were divided into two parts: (1) Training-Sample: 90% of the training data was used to optimize the global parameters of the model and (2) Hyperparameters-Sample: the remaining 10% of the training data was used to fine-tune the hyperparameters of the model (such as the number of neurons), and this part of the data maintains sample balance. The schematic diagram of attention deep learning algorithm is shown in Figure 6 .
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