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Peshnaja: a framework for predicting survivability of glioblastoma patients using ML and SEER data
  • +5
  • Aleema Ashfaq,
  • Dr. Bilal Wajid ,
  • Faria Anwar,
  • Fahim Gohar Awan,
  • Ali Anwar,
  • Muhammad Ali Subhani,
  • Imran Wajid,
  • Abdul Rauf Anwar
Aleema Ashfaq
Ibn Sina Research & Development Division
Dr. Bilal Wajid

Corresponding Author:[email protected]

Author Profile
Faria Anwar
Out-Patient Department, Mayo Hospital
Fahim Gohar Awan
Department of Electrical Engineering, University of Engineering & Technology (UET), New Campus
Ali Anwar
Department of Computer Science & Engineering, University of Minnesota
Muhammad Ali Subhani
Ibn Sina Research & Development Division
Imran Wajid
School of Social Sciences, Istanbul Commerce University
Abdul Rauf Anwar
CENIR MEG-EEG, Paris Brain Institute


Glioblastoma is a common and fatal tumor presenting a poor survival rate. To choose the best course of treatment, patients and providers need to predict the survival rate of patients. Historically, statistical methods have helped analyze clinical features to forecast survival, while recently the same is being accomplished by applying artificial intelligence techniques. However, most of these works are limited to predicting 1-, 2-, or 10-year survivability with several of these works simulating data for balancing the dataset. Hence, there is a need for fine-grained prognosis without tempering the data. To achieve the same, we employ data from Surveillance, Epidemiology, and End Results (SEER) along with an ensemble of classification and regression models to develop a fine-grained model to predict the survival period of glioblastoma patients. The proposed framework titled 'Peshnaja' presents higher resolution in the prognosis of glioblastoma while showcasing an accuracy of 70% with an overall RMSE of 2.65. Moreover, a comparison of Peshnaja with other frameworks shows that we did not impute missing values nor employed synthetic data to force good results, thereby keeping Peshnaja true to the existing data.
09 Jan 2024Submitted to TechRxiv
18 Jan 2024Published in TechRxiv