基于机器学习的面部运动神经传导检查数据的研究及应用

Research and Application of Facial Motor Nerve Conduction Examination Data based on Machine Learning

为初步研究面部运动神经传导检查数据,提出运用机器学习方法进行深度数据挖掘、分析,找出相关性最高的特征值,以研究其主要的影响因素及探讨临床诊断预测的可能性。收集成都中医药大学附属医院10个月的肌电检查报告共2352份数据,筛选符合标准的575份报告,制作数据集,利用编程的方式对其检查数据和报告结论进行量化分析,分别建立KNN、逻辑回归、随机森林、stacking算法模型,经过调参选取正确率最高的模型进行特征提取以研究其主要影响因素及研究临床判读预测的可能性。实验结果表明,一方面在肌电图临床判读中随机森林算法正确率达到92.69%,精度为92.78%,召回率为100%,与逻辑回归相比较P值为0.04271,与KNN相比较P值为0.00745,均具有显著统计学意义,即随机森林模型最适合于面部运动传导神经检查数据分析。另一方面,运用随机森林方法提取特征值,能够更加清晰迅速地找出影响面部运动神经病变的最主要因素。通过机器学习挖掘数据,得出影响面部运动神经传导检查的主要因素为颞支右侧波幅数据和颊支右侧波幅等8个特征点,并提出可使用临床获得的数据集进行判读预测并通过随机森林选取主要的特征点,具体以减少临床操作时检查点位的形式达到缩短单人检查时间的目的。

In order to conduct a preliminary study on facial motor nerve conduction examination data,the method of machine learning is proposed to conduct in-depth data mining and analysis to find the characteristic values with the highest correlation,so as to study the main influencefactors and explore the possibility of clinical diagnosis prediction.We collected 10 months electrical inspection report totally 2352 datafromchengdu university of traditional Chinese medicine medical and selected 575 reports accord with standard to make datasets,we use programming to quantitatively analyze the inspection data and report conclusion,and building KNN,logistic regression,random forests,stacking algorithm model respectively.The model with highest accuracy is selected to extract features after adjustment of arguments,toresearch the main influence factors and the clinical interpretation prediction possibilities.Experimental results show that,on the one hand,random forest algorithm in emg clinical interpretation accuracy reached 92.69%,the precision is 92.78%,the recall rate was 100%,P value is 0.04271 compared with logistical regression and 0.00745 compared with KNN,mentioned above all make great sense.,namely the random forest model is best suited for facial motor conduction nerve examination data analysis,on the other hand,the use of random forests method to extract the characteristic value,can more clear quickly find out the main factors affecting facial movement neuropathy.This research mines data through machine learning,it is concluded that the main factors affecting facial motor nerve conduction check are temporal right amplitude data and buccal branch on the right side of amplitude and so on eight feature points.It is proposed that the clinical data sets can be used for interpretation prediction.By randomly selecting main feature points,specifically reduce the inspection points in clinical operations,to achieve the goal of reducing single person inspection time.