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.
Article
ي Zh
| WPRIM
| ID: wpr-357662
المكتبة المسؤولة:
WPRO
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.
النص الكامل:
1
الفهرس:
WPRIM
الموضوع الرئيسي:
Trace Elements
/
Barium
/
Blood
/
Algorithms
/
Calcium
/
Diagnosis, Computer-Assisted
/
Neural Networks, Computer
/
Computational Biology
/
Copper
/
Diagnosis
نوع الدراسة:
Diagnostic_studies
/
Prognostic_studies
المحددات:
Humans
اللغة:
Zh
مجلة:
Journal of Biomedical Engineering
السنة:
2007
نوع:
Article