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PARKİNSON HASTALIĞINI YÜKSEK DOĞRULUKLA TESPİT ETMEK İÇİN DERİN ÖĞRENME ALGORİTMASININ KULLANIMI

Yıl 2019, Cilt: 22 - Özel Sayı, 19 - 25, 29.11.2019

Öz

Hem motor hem de
motor dışı semptomlarda hayati ve kalıcı hasara neden olan Parkinson
hastalığının erken teşhisi, hasta durumunun daha da kötüleşmesini önlemek için
çok önemlidir. Bu çalışmada, UCI deposundan alınan Parkinson Hastalığı verileri
derin öğrenme mimarisi kullanılarak sınıflandırılmıştır. Çalışmadaki derin
öğrenme mimarisi, Python Keras 
tarafından oluşturulan ileri beslemeli bir sinir ağıdır (FFNN).
Çalışmadaki mimari, bir girdi katmanı, iki gizli katman ve softmax fonksiyonunu
ReLu (Rectified Linear Units) ile bir çıkış katmanı olarak oluşturulmaktadır.
Derin öğrenme mimarisi, PD veri seti iki sınıfa sahip olduğundan dolayı, ikili
veri sınıflandırma problemini çözer. PD veri setini sınıflandırmak için test ve
eğitim verisi farklı oranlarda bölünerek birçok test yapıldı. PD veri seti
sınıflandırması, % 20'sinde test ve kalan veri eğitim verisi olmak üzere, derin
öğrenme algoritması kullanılarak % 100 doğrulukta başarılı oldu.

Kaynakça

  • Agarap, A. F. (2018). Deep Learning using Rectified Linear Units (ReLU), Neural and Evolutionary Computing, Vol. 1.
  • Beale, M. H., Hagan, M. T., & Demuth, H. B. (2010). Neural network toolbox. User’s Guide, MathWorks, 2, 77-81.
  • Ben-Bright, B., Zhan, Y., Ghansah, B., Amankwah, R., Wornyo, D. K., & Ansah, E. (2017). Taxonomy and a Theoretical Model for Feedforward Neural Networks. International Journal of Computer Applications, 975, 8887.
  • Chen, H. L., Huang, C. C., Yu, X. G., Xu, X., Sun, X., Wang, G., & Wang, S. J. (2013). An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert systems with applications, 40(1), 263-271.
  • Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514-525.
  • Das, R. (2010). A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications, 37(2), 1568-1572.
  • David Gil, A., & Maguns Johnson, B. (2004). Diagnosing Parkinson by Using Artificial Neural Networks and Support Vector Machines. Global Journal of Computer Science and Technology, 63-71.
  • Gharehchopogh, F. S., & Mohammadi, P. (2013). A Case Study of Parkinson’s disease Diagnosis using Artificial Neural Networks. International Journal of Computer Applications, 73(19), 0975 – 8887.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in bioinformatics, 18(5), 851-869.
  • Okun, M. S. (2012). Deep-brain stimulation for Parkinson's disease. New England Journal of Medicine, 367(16), 1529-1538.
  • Sakar C.O., & Kursun, O. (2009). Telediagnosis of Parkinson’s disease using measurements of dysphonia, J. Med. Syst., 34 (4) 591–599.
  • Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Tutuncu,M., Aydin, T., Isenkul, M. E., & Apaydin, H. (2019). A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform, Applied Soft Computing, 74, 255-263.
  • Sprenger, F., & Poewe, W. (2013). Management of motor and non-motor symptoms in Parkinson’s disease, CNS drugs, 27.4, 259-272.
  • Srinivasan, S. M., Martin, M., & Tripathi, A. (2017). ANN based Data Mining Analysis of the Parkinson’s Disease. International Journal of Computer Applications, 168(1).

USING DEEP LEARNING ALGORITHM TO DIAGNOSE PARKINSON DISEASE WITH HIGH ACCURACY

Yıl 2019, Cilt: 22 - Özel Sayı, 19 - 25, 29.11.2019

Öz

Early diagnosis of Parkinson's disease, which causes
vital and permanent damage to both motor and non-motor symptoms, is very
important to prevent further deterioration of the patient condition. In the
present study, Parkinson's Disease data set from UCI repository is classified
using deep learning architecture. The deep learning architecture in the study
is a feed-forward neural network (FFNN) which is builded by Keras of Python.
The architecture in the study composes of an input layer, two hidden layers and
softmax function with ReLu (Rectified Linear Units) as an output layer. The
deep learning architecture solves binary classification problem since PD data
set has two classes. In order to classify the PD data set, many tests were
performed by splitting the test and train data in different ratios. The PD data
set classification was succeeded with 100% accuracy using deep learning
algorithm splitting in %20 of the data as the test and the remaining as train
data in epoch 30.

Kaynakça

  • Agarap, A. F. (2018). Deep Learning using Rectified Linear Units (ReLU), Neural and Evolutionary Computing, Vol. 1.
  • Beale, M. H., Hagan, M. T., & Demuth, H. B. (2010). Neural network toolbox. User’s Guide, MathWorks, 2, 77-81.
  • Ben-Bright, B., Zhan, Y., Ghansah, B., Amankwah, R., Wornyo, D. K., & Ansah, E. (2017). Taxonomy and a Theoretical Model for Feedforward Neural Networks. International Journal of Computer Applications, 975, 8887.
  • Chen, H. L., Huang, C. C., Yu, X. G., Xu, X., Sun, X., Wang, G., & Wang, S. J. (2013). An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert systems with applications, 40(1), 263-271.
  • Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514-525.
  • Das, R. (2010). A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications, 37(2), 1568-1572.
  • David Gil, A., & Maguns Johnson, B. (2004). Diagnosing Parkinson by Using Artificial Neural Networks and Support Vector Machines. Global Journal of Computer Science and Technology, 63-71.
  • Gharehchopogh, F. S., & Mohammadi, P. (2013). A Case Study of Parkinson’s disease Diagnosis using Artificial Neural Networks. International Journal of Computer Applications, 73(19), 0975 – 8887.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in bioinformatics, 18(5), 851-869.
  • Okun, M. S. (2012). Deep-brain stimulation for Parkinson's disease. New England Journal of Medicine, 367(16), 1529-1538.
  • Sakar C.O., & Kursun, O. (2009). Telediagnosis of Parkinson’s disease using measurements of dysphonia, J. Med. Syst., 34 (4) 591–599.
  • Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Tutuncu,M., Aydin, T., Isenkul, M. E., & Apaydin, H. (2019). A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform, Applied Soft Computing, 74, 255-263.
  • Sprenger, F., & Poewe, W. (2013). Management of motor and non-motor symptoms in Parkinson’s disease, CNS drugs, 27.4, 259-272.
  • Srinivasan, S. M., Martin, M., & Tripathi, A. (2017). ANN based Data Mining Analysis of the Parkinson’s Disease. International Journal of Computer Applications, 168(1).
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Araştırma Makalesi
Yazarlar

Fahriye Gemcı 0000-0003-0961-5266

Turgay Ibrıkcı 0000-0003-1321-2523

Yayımlanma Tarihi 29 Kasım 2019
Gönderilme Tarihi 22 Temmuz 2019
Yayımlandığı Sayı Yıl 2019Cilt: 22 - Özel Sayı

Kaynak Göster

APA Gemcı, F., & Ibrıkcı, T. (2019). USING DEEP LEARNING ALGORITHM TO DIAGNOSE PARKINSON DISEASE WITH HIGH ACCURACY. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 22, 19-25.