Lung cancer survival period prediction and understanding: Deep learning approaches.

Journal: International journal of medical informatics
Published Date:

Abstract

INTRODUCTION: Survival period prediction through early diagnosis of cancer has many benefits. It allows both patients and caregivers to plan resources, time and intensity of care to provide the best possible treatment path for the patients. In this paper, by focusing on lung cancer patients, we build several survival prediction models using deep learning techniques to tackle both cancer survival classification and regression problems. We also conduct feature importance analysis to understand how lung cancer patients' relevant factors impact their survival periods. We contribute to identifying an approach to estimate survivability that are commonly and practically appropriate for medical use.

Authors

  • Shreyesh Doppalapudi
    The Big Data Lab, Division of Engineering and Information Science, The Pennsylvania State University, Great Valley, Malvern, PA, 19355, USA. Electronic address: dshreyesh@psu.edu.
  • Robin G Qiu
  • Youakim Badr
    The Big Data Lab, Division of Engineering and Information Science, The Pennsylvania State University, Great Valley, Malvern, PA, 19355, USA. Electronic address: yzb61@psu.edu.