MNCS.csv

In order to study the application of machine learning in myoelectric data, the machine learning method has been used for data mining and analysis so as to find correlation characteristics. More than 2,300 myoelectric examination data from Sichuan Provincial Hospital of Traditional Chinese Medicine (TCM) for 10 months has been collected and recorded. By means of setting the inclusion criteria and excluding the irrelevant factors, the facial nerve electromyography and auditory brainstem response test reports that meet the research criteria have been screened out. Among them, there were 575 facial nerve electromyography reports meeting the research criteria, and 233 auditory brainstem response (ABR) reports in all. Based on these reports, the data sets have been established. On the one hand, by comparing the advantages and disadvantages of several algorithms in EMG examination, the conclusions of random forest optimality are obtained. Meanwhile, the clinically obtained data are used for the interpretation and prediction. The results verify the clinical potential of machine learning in diagnosis and diagnostic assessment. On the other hand, the eigenvalues extracted by the random forest algorithm are adopted to obtain the facial nerve electromyography and the most important influencing factors of ABR, which facilitates the clinical analysis of doctors.