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_27
Snippet: Diagnostic results of hyperlipidemia samples were quantified as 1, and health was 0. The parameter order of the feature sequence has not been specially designed. LSTM model can automatically learn the joint features between parameters that are close or far apart. Because there are complex internal relations between different physiological parameters, the LSTM model is a better choice......
Document: Diagnostic results of hyperlipidemia samples were quantified as 1, and health was 0. The parameter order of the feature sequence has not been specially designed. LSTM model can automatically learn the joint features between parameters that are close or far apart. Because there are complex internal relations between different physiological parameters, the LSTM model is a better choice.
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