Comparison of Principal Component Analysis and Radial Basis Function Network for Diagnosis of Hypertension

Turk F., BARIŞÇI N., Ciftci A., Ekmekci Y.

9th International Conference on Electronics Computer and Computation (ICECCO 2012), Ankara, Türkiye, 1 - 03 Kasım 2012, ss.33-36 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Basıldığı Şehir: Ankara
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.33-36
  • Gazi Üniversitesi Adresli: Hayır

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.