Abstract
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.
References
- 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).
Abstract
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.
Keywords
Binary classification deep learning (DL) feed-forward neural network (FFNN) medical diagnosis parkinson disease (PD)
References
- 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).
Details
| Primary Language | English |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | November 29, 2019 |
| Submission Date | July 22, 2019 |
| Published in Issue | Year 2019Volume: 22 - Special Issue |
