This work presents a survivability prediction model for colon cancer developed
with machine learning techniques. Survivability was viewed as a classification
task where it was necessary to determine if a patient would survive each of
the five years following treatment. The model was based on the SEER dataset
which, after preprocessing, consisted of 38,592 records of colon cancer patients.
Six features were extracted from a feature selection process in order to construct
the model. This model was compared with another one with 18 features
indicated by a physician. The results show that the performance of the sixfeature
model is close to that of the model using 18 features, which indicates
that the first may be a good compromise between usability and performance.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a
Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The work of Tiago Oliveira is supported
by a FCT grant with the reference SFRH/BD/85291/ 2012.info:eu-repo/semantics/publishedVersio