基于机器学习的前庭康复决策研究

Research on modelling vestibular rehabilitation decision based on machine learning

目的:应用支持向量机(SVM)和人工神经网络(ANN)搭建前庭康复治疗决策模型,以期为临床提供决策支撑和参考。方法:选择感觉组合测试(SOT)总分COMP、本体觉得分、视觉得分、前庭觉得分和眩晕障碍量表(DHI)的测试结果躯体得分DHI-P、情感得分DHI-E、功能得分DHI-F作为SVM和ANN模型的输入,推荐康复方案作为输出。根据文献数据源,仿真数据作为样本集进行模型训练,以实测样本为测试集检验模型准确性。结果:BP神经网络模型准确率为52.3%,SVM模型准确率为83.4%。误差主要来源于3种诊断方案下各项得分数据区间交叠严重,易造成边界样本点误分类,这在临床诊断中也是很难克服的问题。结论:基于SVM建立的前庭康复决策方案准确性高于ANN,具有一定的临床应用价值,利用机器学习,辅助决策前庭康复方案,在推进临床医疗信息化、提升医疗质量方面有重要的前瞻性参考意义。

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.