Application of Linear Mixed Effects Model Based on BP-L2 Hybrid Algorithm in Speech Signals of Parkinson's Disease
By preprocessing and analyzing longitudinal high-dimensional data of multicollinearity in Parkinson's disease speech signals,important information can be provided for relevant medical institutions.Firstly,the data is preprocessed using Random Forest to obtain a reduced dimensional dataset.Through correlation analysis,it is found that the data has multicollinearity issues.Then,L2 regularization is introduced into the BP Neural Networks to change its objective function,solving the problem of data multicollinearity in clinical data,so as to better fit the linear mixed effects model.Finally,a comparative analysis is conducted on the AIC,BIC,and-LogLik indicators of the linear mixed effects model before and after the introduction of the BP-L2 hybrid algorithm,demonstrating the advantages of introducing this algorithm.
Parkinson's diseaseRandom ForestL2 regularizationBP Neural Networkslinear mixed effects model
万方数据
