Automatic Diagnosis of Parkinson's Disease Using Principal Component Analysis Methods

Year 2019, Issue: 16, 294 - 300, 31.08.2019

Şule Yücelbaş , Cüneyt Yücelbaş

https://doi.org/10.31590/ejosat.568544

Cited By: 5

Abstract

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. 

Keywords

Parkinson's disease, dimension reduction methods, TQWT, PCA

Temel Bileşen Analizi Yöntemleri Kullanarak Parkinson Hastalığının Otomatik Teşhisi

Abstract

Parkinson rahatsızlığı çok
yavaş ilerleyen sinsi bir beyin hastalığıdır. Bu hastalığın teşhis yöntemleri
arasında kişilere ait seslerin analizi de bulunmaktadır. Ses analizi ile
Parkinson’nun en erken tespiti kullanılan çeşitli yöntemler sayesinde mümkün olmaktadır.
Bu çalışma kapsamında 188 Parkinson hastası ve 64 sağlıklı kişiye ait
kaydedilmiş ses sinyallerine Ayarlanabilir Q-faktör Dalgacık Dönüşümü (AQDD)
metodu uygulanması sonucunda elde edilen özellikler kullanılmıştır. AQDD
özelliklerine, boyut indirgeme yöntemlerinden temel bileşen analizi (TBA) ve
bunun çeşitlerinden olan kernel TBA (KTBA) ile olasılıksal TBA (OTBA)
uygulanmıştır. Daha sonra boyutları indirgenen yeni veri gruplarına ayrı ayrı
k-kat çapraz doğrulama yöntemi uygulanarak eğitim-test verileri elde
edilmiştir. Sonraki aşamada ise, boyut indirgeme yöntemlerinin etkinliğinin
araştırılması için veriler rastgele orman (RO) algoritması ile ayrı ayrı
sınıflandırılmış ve elde edilen sonuçlar ayrıca istatistiksel ölçütlerle
yorumlanmıştır. Sınıflandırma sonuçları açısından boyut azaltma yöntemleri
içerisinde en başarılısı %87.56 doğruluk oranı ile OTBA olmuştur. Ayrıca bu
yöntem sonucunda ROC ve PRC alan değerleri yaklaşık 0.95 bandına ulaşarak hasta
ve sağlıklı sınıf ayrışımının mükemmele yaklaştığını kanıtlamıştır. Gerçek
yaşam uygulamalarına uygun olan bu çalışmanın performans sonuçları, aynı
verinin kullanıldığı literatürdeki tek çalışma ile kıyaslanmış ve bu çalışmada
diğer çalışmaya nazaran daha yüksek istatistiksel oranların elde edildiği görülmüştür.
Ayrıca verilerin kaydedildiği kişi sayısının literatürdeki diğer çalışmalara
göre yüksek oluşu çalışmanın bu alandaki önemini arttırmaktadır. 

Keywords

Parkinson hastalığı, boyut indirgeme yöntemleri, AQDD, TBA

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