Research on modelling vestibular rehabilitation decision based on machine learning

Abstract

Objective:To evaluate the effect of the support vector machine（SVM） and artificial neutral network（ANN） on the treatment choice of vestibular rehabilitation. Method:Total scores COMP and three ratios of sensory analysis: somatosensory（SOM）, visual（VIS）, vestibular（VEST） from the sensory organization test（SOT）, and physical score（DHI-P）, emotional score（DHI-E）, functional score（DHI-F） from the dizziness handicap inventory（DHI） were chosen as input of SVM and ANN, rehabilitation program as output. According to the data source of the literatures, we constructed simulation database used as the sample set to conduct model training, and part of the clinical data was used to train the model accuracy. Result:After trainings, the accuracy rate of ANN model was 52.3%, and that of SVM model was 83.4%. The error mainly comes from the serious overlap of each score data interval under the three diagnostic schemes, which easily leads to the misclassification of boundary sample points, which is also a difficult problem to overcome in clinical diagnosis. Conclusion:Vestibular rehabilitation decision based SVM is more accurate than ANN. The use of machine learning to assist decision-making of vestibular rehabilitation scheme has important prospective reference significance in promoting clinical medical informatization and improving medical quality.