Neural Network Design for the Recurrence Prediction of Post-Operative Non-Metastatic Kidney Cancer Patients

Tander B., ÖZMEN A., Özden E.

9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Türkiye, 26 - 28 Kasım 2015, ss.162-165 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Bursa
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.162-165
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

In this paper, various post-operative recurrence estimation models called nomograms for the kidney cancer patients without any metastates are introduced and novel systems based on a Multilayer Perceptron Neural Network are designed to simplify and integrate the mentioned techniques which is believed to ease the physician's post-operative follow up procedures. The parameters effecting the recurrence are the TNM stage, tumor size and nuclear (Fuhrman) grade, the existance of necrosis and vascular invasion. Independent systems for two of the individual prediction methods, as well as a system that combines these are designed and performance analyses are carried out to verify the reliability.