USING DEEP LEARNING ALGORITHM TO DIAGNOSE PARKINSON DISEASE WITH HIGH ACCURACY
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.
Anahtar Kelimeler
Kaynakça
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Ayrıntılar
Birincil Dil
İngilizce
Konular
Bilgisayar Yazılımı
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
29 Kasım 2019
Gönderilme Tarihi
22 Temmuz 2019
Kabul Tarihi
17 Ekim 2019
Yayımlandığı Sayı
Yıl 2019 Cilt: 22