Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4958290 | Computer Methods and Programs in Biomedicine | 2016 | 9 Pages |
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 ClÃnico Universitario (Málaga, Spain) considering a follow up period from 1982 to 2008.
Keywords
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Iván Gómez, Nuria Ribelles, Leonardo Franco, Emilio Alba, José M. Jerez,