SVM-aided cancer diagnosis based on the concentration of the macroelement and microelement in human blood / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 513-518, 2007.
Artigo
em Chinês
| WPRIM
| ID: wpr-357662
ABSTRACT
Support vector machine (SVM) has shown its excellent learning and generalization ability for the binary classification of real problems and has been extensively employed in many areas. In this paper, SVM, K-Nearest Neighbor, Decision Tree C4.5 and Artificial Neural Network were applied to identify cancer patients and normal individuals using the concentrations of 6 elements including macroelements (Ca, Mg) and microelements (Ba, Cu, Se, Zn) in human blood. It was demonstrated, by using the normalized features instead of the original features, the classification performances can be improved from 91.89% to 95.95%, from 83.78% to 93.24%, and from 90.54% to 94.59% for SVM, K-NN and ANN respectively, whereas that of C4.5 keeps unchangeable. The best average accuracy of SVM with linear dot kernel by using 5-fold cross validation reaches 95.95%, and is superior to those of other classifiers based on K-NN (93.24%), C4.5 (79.73%), and ANN (94.59%). The study suggests that support vector machine is capable of being used as a potential application methodology for SVM-aided clinical cancer diagnosis.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Oligoelementos
/
Bário
/
Sangue
/
Algoritmos
/
Cálcio
/
Diagnóstico por Computador
/
Redes Neurais de Computação
/
Biologia Computacional
/
Cobre
/
Diagnóstico
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2007
Tipo de documento:
Artigo
Similares
MEDLINE
...
LILACS
LIS