Comparison of Principal Component Analysis and Radial Basis Function Network for Diagnosis of Hypertension
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Tarih
2012
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Turgut Ozal Univ
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, from 150 individuals over the age of 30 taken no drugs, sex, age, height, weight, HDL, LDL, Triglyceride, smoking and uric acid were measured. 65 of them are normal but 85 consist of the patients. Data obtained of each patient was applied Artificial Neural Network (ANN) models. The results obtained will be classified as either normal or the patient. Using Principal Component Analysis (PCA), 89% of patient individuals and 88% of normal individuals were classified correctly. Using Radial Basis Function Networks (RBFN), 89% of the patient individuals and 84% of normal individuals were classified correctly.
Açıklama
9th International Conference on Electronics Computer and Computation (ICECCO 2012) -- NOV 01-03, 2012 -- Ankara, TURKEY
Anahtar Kelimeler
Principal Component Analysis, PCA, Radial Basis Function Networks, RBFN, Hypertension
Kaynak
Icecco'12: 9Th International Conference On Electronics, Computer And Computation
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
Sayı
Künye
closedAccess
