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Treating colon cancer survivability prediction as a classification problem

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Resumo: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.
Autores principais:Silva, Ana Paula Pinto da
Outros Autores:Oliveira, Tiago José Martins; Neves, José; Novais, Paulo
Assunto:Colon cancer Prediction Machine learning Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Ano:2016
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

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