TY  - JOUR
T1  - USING DEEP LEARNING ALGORITHM TO DIAGNOSE PARKINSON DISEASE WITH HIGH ACCURACY
TT  - PARKİNSON HASTALIĞINI YÜKSEK DOĞRULUKLA TESPİT ETMEK İÇİN DERİN ÖĞRENME ALGORİTMASININ KULLANIMI
AU  - Gemcı, Fahriye
AU  - Ibrıkcı, Turgay
PY  - 2019
DA  - November
DO  - 10.17780/ksujes.594977
JF  - Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi
JO  - KSU J. Eng. Sci.
PB  - Kahramanmaras Sutcu Imam University
WT  - DergiPark
SN  - 1309-1751
SP  - 19
EP  - 25
VL  - 22
LA  - en
AB  - Early diagnosis of Parkinson's disease, which causesvital and permanent damage to both motor and non-motor symptoms, is veryimportant to prevent further deterioration of the patient condition. In thepresent study, Parkinson's Disease data set from UCI repository is classifiedusing deep learning architecture. The deep learning architecture in the studyis 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 andsoftmax function with ReLu (Rectified Linear Units) as an output layer. Thedeep learning architecture solves binary classification problem since PD dataset has two classes. In order to classify the PD data set, many tests wereperformed by splitting the test and train data in different ratios. The PD dataset classification was succeeded with 100% accuracy using deep learningalgorithm splitting in %20 of the data as the test and the remaining as traindata in epoch 30.
KW  - Binary classification
KW  - deep learning (DL)
KW  - feed-forward neural network (FFNN)
KW  - medical diagnosis
KW  - parkinson disease (PD)
N2  - Hem motor hem demotor dışı semptomlarda hayati ve kalıcı hasara neden olan Parkinsonhastalığının erken teşhisi, hasta durumunun daha da kötüleşmesini önlemek içinçok önemlidir. Bu çalışmada, UCI deposundan alınan Parkinson Hastalığı verileriderin öğrenme mimarisi kullanılarak sınıflandırılmıştır. Çalışmadaki derinöğrenme mimarisi, Python Kerastarafından oluşturulan ileri beslemeli bir sinir ağıdır (FFNN).Çalışmadaki mimari, bir girdi katmanı, iki gizli katman ve softmax fonksiyonunuReLu (Rectified Linear Units) ile bir çıkış katmanı olarak oluşturulmaktadır.Derin öğrenme mimarisi, PD veri seti iki sınıfa sahip olduğundan dolayı, ikiliveri sınıflandırma problemini çözer. PD veri setini sınıflandırmak için test veeğitim verisi farklı oranlarda bölünerek birçok test yapıldı. PD veri setisınıflandırması, % 20'sinde test ve kalan veri eğitim verisi olmak üzere, derinöğrenme algoritması kullanılarak % 100 doğrulukta başarılı oldu.
CR  - Agarap, A. F.  (2018). Deep Learning using Rectified Linear Units (ReLU), Neural and Evolutionary Computing, Vol. 1.
CR  - Beale, M. H., Hagan, M. T., & Demuth, H. B. (2010). Neural network toolbox. User’s Guide, MathWorks, 2, 77-81.
CR  - 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.
CR  - 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.
CR  - Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514-525.
CR  - Das, R. (2010). A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications, 37(2), 1568-1572.
CR  - 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.
CR  - 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.
CR  - LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
CR  - Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in bioinformatics, 18(5), 851-869.
CR  - Okun, M. S. (2012). Deep-brain stimulation for Parkinson's disease. New England Journal of Medicine, 367(16), 1529-1538.
CR  - Sakar C.O., & Kursun, O. (2009). Telediagnosis of Parkinson’s disease using measurements of dysphonia, J. Med. Syst., 34 (4) 591–599.
CR  - 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.
CR  - Sprenger, F., & Poewe, W. (2013). Management of motor and non-motor symptoms in Parkinson’s disease, CNS drugs, 27.4, 259-272.
CR  - 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).
UR  - https://doi.org/10.17780/ksujes.594977
L1  - https://dergipark.org.tr/en/download/article-file/857628
ER  -