Paper Info
Title
Survival model in oral squamous cell carcinoma based on clinicopathological parameters, molecular markers and support vector machines
Abstract
The aim of the present study is to find an intelligent and efficient model, based on Support Vector Machines (SVM), able to predict prognosis in patients with oral squamous cell carcinoma (OSCC). A total of 34 clinical and molecular variables were studied in 69 patients suffering from an OSCC. Variables were selected by means of two methods applied in parallel (Non-concave penalty and Newton's methods). The implementation of a predictive model was performed using the SVM as a classifier algorithm. Finally, its classification ability was evaluated by discriminant analysis. Recurrence, number of recurrences, and TNM stage have been identified as the most relevant prognosis factors with both used methods. Classification rates reached 97.56% and 100% for alive and dead patients, respectively (overall classification rate of 98.55%). SVM techniques build tools able to predict with high accuracy the survival of a patient with OSCC.
Year
DOI
Venue
2013
10.1016/j.eswa.2013.02.032
Expert Syst. Appl.
Keywords
Field
DocType
support vector machine,classification rate,support vector machines,tnm stage,molecular marker,oral squamous cell carcinoma,clinicopathological parameter,non-concave penalty,predictive model,classification ability,overall classification rate,efficient model,relevant prognosis factor,survival model,svm technique,immunohistochemistry
Computer science,Support vector machine,Artificial intelligence,Linear discriminant analysis,Classifier (linguistics),Survival analysis,Classification rate,Carcinoma,Machine learning
Journal
Volume
Issue
ISSN
40
12
0957-4174
Citations 
PageRank 
References 
2
0.42
11
Authors
6
Name
Order
Citations
PageRank
Pablo Rosado120.42
Paloma Lequerica-FernáNdez220.42
Lucas VillallaíN320.42
Ignacio PeñA421.10
Fernando Sanchez-Lasheras520.42
Juan C. De Vicente620.42