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Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression
المؤلفون المشاركون
Mahjub, Hossein
Goli, Shahrbanoo
Faradmal, Javad
Mashayekhi, Hoda
Soltanian, Ali-Reza
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-11-01
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1100077
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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