Patient pulse condition diagnosis system based on neural network
A neural network and diagnostic system technology, applied in the field of pulse diagnosis system, can solve problems such as difficulty in gaining trust in doctor's pulse diagnosis level and difficulty in improving pulse diagnosis ability training, and achieve the effects of saving time and labor costs, stabilizing the model, and ensuring accuracy
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[0033] The present invention will be further described below with reference to the accompanying drawings, so that those skilled in the art can refer to the description text to implement the present invention.
[0034] The invention works in the Ubuntu16.04.4LTS environment, and uses PyTorch as the framework to build. The main parameters are: the initial learning rate is 0.001, the momentum parameter is 0.942, the weight coefficient is 0.0005, and the training threshold is 0.62. Diversity, corresponding data enhancement is carried out on the fluctuation frequency and fluctuating vibration of the pulse, and each step selects whether to use it or not with a probability of 0.5.
[0035] The technical scheme adopted in the present invention is: a neural network-based patient pulse condition diagnosis system, comprising the following steps:
[0036] (1) Acquisition of patient’s wrist pulse information data;
[0037] (2) The division of the patient's wrist pulse information samples;...
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