Supervised discretization can discover risk groups in cancer survival analysis. (November 2016) - Gómez, Iván; Ribelles, Nuria; Franco, Leonardo; Alba, Emilio; Jerez, José M.
Highlights: TNM staging system remains the main prognostic classification in many adult cancers in order to predict treatment outcomes. Machine learning algorithms are performed to find different patient groups in the treatment of breast cancer disease. The new approaches found novel group risks that improve prediction results of breast cancer relapse. Abstract: Discretization of continuous variables is a common practice in medical research to identify risk patient groups. This work compares the performance of gold-standard categorization procedures (TNM+A protocol) with that of three supervised discretization methods from Machine Learning (CAIM, ChiM and DTree) in the stratification of patients with breast cancer. The performance for the discretization algorithms was evaluated based on the results obtained after applying standard survival analysis procedures such as Kaplan–Meier curves, Cox regression and predictive modelling. The results show that the application of alternative discretization algorithms could lead the clinicians to get valuable information for the diagnosis and outcome of the disease. Patient data were collected from the Medical Oncology Service of the Hospital Clínico Universitario (Málaga, Spain) considering a follow up period from 1982 to 2008.