Authors:
Aishwarya Mohan
and
Aleksandar Jeremic
Affiliation:
Department of Electrical and Computer Engineering McMaster University, Hamilton, ON, Canada
Keyword(s):
Survival Prediction, Logistic Regression, Machine Learning.
Abstract:
Lung cancer is the leading cause among cancer-related deaths worldwide. Clinically, it could be divided into several groups: 1) the non-small cell lung cancer (NSCLC, 83.4%), 2) the small cell lung cancer (SCLC,13.3%), 3) not otherwise specified lung cancer (NOS,3.1%), 4) aarcoma lung carcinoma (0.2%), and 5) other specified carcinoma (0.1%). According to SEER Cancer Statistics Review, 5-year survival rate of patients with advanced non-small cell lung cancer (NSCLC) who received chemotherapy was less than 5%. Our ability to provide survival status at any time in future is important from at least two standpoints: a) from the clinical standpoint it enables clinicians to provide optimal delivery of healthcare and b) from a personal standpoint, by providing patient’s family with opportunities to plan their life ahead and potentially cope with emotional aspect of loss of life. In this paper we propose to utilize machine learning techniques to achieve this goal and evaluate several techniq
ues in order to determine their prediction performance using publicly available dataset.
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