Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression
Joint Authors
Mahjub, Hossein
Goli, Shahrbanoo
Faradmal, Javad
Mashayekhi, Hoda
Soltanian, Ali-Reza
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-01
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The Support Vector Regression (SVR) model has been broadly used for response prediction.
However, few researchers have used SVR for survival analysis.
In this study, a new SVR model is proposed and SVR with different kernels and the traditional Cox model are trained.
The models are compared based on different performance measures.
We also select the best subset of features using three feature selection methods: combination of SVR and statistical tests, univariate feature selection based on concordance index, and recursive feature elimination.
The evaluations are performed using available medical datasets and also a Breast Cancer (BC) dataset consisting of 573 patients who visited the Oncology Clinic of Hamadan province in Iran.
Results show that, for the BC dataset, survival time can be predicted more accurately by linear SVR than nonlinear SVR.
Based on the three feature selection methods, metastasis status, progesterone receptor status, and human epidermal growth factor receptor 2 status are the best features associated to survival.
Also, according to the obtained results, performance of linear and nonlinear kernels is comparable.
The proposed SVR model performs similar to or slightly better than other models.
Also, SVR performs similar to or better than Cox when all features are included in model.
American Psychological Association (APA)
Goli, Shahrbanoo& Mahjub, Hossein& Faradmal, Javad& Mashayekhi, Hoda& Soltanian, Ali-Reza. 2016. Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1100077
Modern Language Association (MLA)
Goli, Shahrbanoo…[et al.]. Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1100077
American Medical Association (AMA)
Goli, Shahrbanoo& Mahjub, Hossein& Faradmal, Javad& Mashayekhi, Hoda& Soltanian, Ali-Reza. Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1100077
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100077