%0 Journal Article
%T Automatic Diagnosis of Parkinson's Disease Using Principal Component Analysis Methods
%A £¿ule Y¨¹celba£¿
%J -
%D 2019
%X Parkinson's disease is a sneaky brain disorder that progresses very slowly. The diagnostic methods of this disease include the analysis of individual voices. The earliest detection of Parkinson's by voice analysis is made possible by various methods. In this study, the results obtained from the application of Tunable Q-factor Wavelet Transformation (TQWT) method to the recorded audio signals of 188 Parkinson's patients and 64 healthy individuals were used. The principal component analysis (PCA) and its types (kernel PCA (KPCA) and Probabilistic PCA (PPCA)) which are dimension reduction methods have been applied to the TQWT features. Afterwards, k-fold cross validation method was applied to the new data groups and the training-test data were obtained. In the next step, the data were separately classified by random forest (RF) algorithm to investigate the effectiveness of the dimension reduction methods and the results were also interpreted by statistical criteria. In terms of classification results, OTBA was the most successful in size reduction methods with 87.56% accuracy rate. In addition, as a result of this method, ROC and PRC area values reached a band of about 0.95, proving that patient and healthy class decomposition approached perfection. The performance results of this study, which is suitable for real-life applications, were compared with the single study in the literature in which the same data was used, and this study showed that higher statistical ratios were obtained in comparison to the other study. Moreover, the high number of people in whom the data was recorded compared to other studies in the literature increases the importance of the study in this field
%K Parkinson hastal£¿£¿£¿
%K boyut indirgeme y£¿ntemleri
%K AQDD
%K TBA
%U http://dergipark.org.tr/ejosat/issue/45333/568544