EFFECT OF DISCRETIZATION METHOD ON THE DIAGNOSIS OF PARKINSON'S DISEASE
Küçük Resim Yok
Tarih
2011
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ICIC INTERNATIONAL
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Implementing different classification methods, this study analyzes the effect of discretization on the diagnosis of Parkinson's disease. Entropy-based discrelization method is used as the discretization method, and support vector machines, C4.5, k-nearest neighbors and Naive Bayes are used as the classification methods. The diagnosis of Parkinson's disease is implemented without using any preprocessing method. Afterwards, the Parkinson's disease dataset is classified after implementing entropy-based discretization on the dataset. Both results are compared, and it is observed that using discretization method increases the success of classification on the diagnosis of Parkinson's disease by 4.1% to 12.8%.
Açıklama
Anahtar Kelimeler
Parkinson's disease, Entropy-based discretization method, Classification methods
Kaynak
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
7
Sayı
8
