Comparison of dimension reduction methods using patient satisfaction data

In this study, we compared classical principal components analysis (PCA), generalized principal components analysis (GPCA), linear principal components analysis using neural networks (PCA-NN), and non-linear principal components analysis using neural networks (NLPCA-NN). Data were extracted from the patient satisfaction query with regard to the satisfaction of patients from hospital staff, which was applied in 2005 at the outpatient clinics of Trakya University Medical Faculty. We found that percentages of explained variance of principal components from PCA-NN and NLPCA-NN were highest for doctor, nurse, radiology technician, laboratory technician, and other staff using a patient satisfaction data set. Results show that methods using NN which have higher percentages of explained variances than classical methods could be used for dimension reduction.