title: Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression.
abstract: 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.
authors.affiliation: Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Shahrbanoo
authors.initials: S
authors.lastname: Goli
authors.affiliation: Research Center for Health Sciences, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Hossein
authors.identifier: 0000-0002-9375-3807
authors.initials: H
authors.lastname: Mahjub
authors.affiliation: Modeling of Non-Communicable Diseases Research Center, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Javad
authors.initials: J
authors.lastname: Faradmal
authors.affiliation: Computer and IT Engineering Department, Shahrood University, Shahrood, Iran.
authors.forename: Hoda
authors.initials: H
authors.lastname: Mashayekhi
authors.affiliation: Modeling of Non-Communicable Diseases Research Center, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Ali-Reza
authors.initials: AR
authors.lastname: Soltanian
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000453:epidemiology* / Q000401:mortality* / Q000628:therapy; D015331:Cohort Studies; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007492:Iran; D016014:Linear Models; D008490:Medical Informatics; D008495:Medical Oncology / Q000379:methods; D009362:Neoplasm Metastasis; D011379:Prognosis; D016016:Proportional Hazards Models; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000235:genetics; D011980:Receptors, Progesterone / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; D011980:Receptors, Progesterone; C508053:ERBB2 protein, human