loading page

Praedico â\euro“ Salvos: an ensemble ML framework for predicting survivability of thyroid cancer patients.
  • +6
  • Fareeha Afzal ,
  • Dr. Bilal Wajid ,
  • Faria Anwar ,
  • umar rashid ,
  • Dr. Fahim G. Awan ,
  • anoosha tahir ,
  • Imr Wajid ,
  • ali anwar ,
  • Dr. Abdul Rauf Anwar
Fareeha Afzal
Author Profile
Dr. Bilal Wajid
Sabz Qalam

Corresponding Author:[email protected]

Author Profile
Faria Anwar
Author Profile
umar rashid
Author Profile
Dr. Fahim G. Awan
Author Profile
anoosha tahir
Author Profile
Imr Wajid
Author Profile
ali anwar
Author Profile
Dr. Abdul Rauf Anwar
Author Profile

Abstract

As the survival rate of cancer patients is low, it is only natural for patients to know how long they will survive. The National Cancer Institute in MD, USA, has developed and maintained the Surveillance, Epidemiology, and End Results (SEER) program for the past four decades. The SEER program incorporates an extensive and growing repository of data related to different types of cancers. Even though the literature presents significant work on predicting the 5-year survivability of cancer patients, there is a need for a fine-grained prognosis. This paper employs the SEER dataset for developing a survivability model for thyroid cancer patients. The proposed framework titled ‘Praedico â\euro“ Salvos’ presents higher resolution in the prognosis of thyroid cancer patients while showcasing an accuracy of 88%.