基于人体血液常/微量元素含量的SVM癌症辅助诊断

Translated title of the contribution: SVM-aided cancer diagnosis based on the concentration of the macroelement and microelement in human blood

Qianfei Yuan, Congzhong Cai, Hanguang Xiao, Xinghua Liu, Yufeng Wen

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageCantonese
Pages (from-to)513-518
Number of pages6
JournalShengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering
Volume24
Issue number3
Publication statusPublished - Jun 2007

Keywords

  • Algorithms
  • Barium
  • Calcium
  • Computational Biology
  • Copper
  • Diagnosis, Computer-Assisted
  • Humans
  • Neoplasms
  • Neural Networks (Computer)
  • Trace Elements
  • English Abstract
  • Journal Article
  • Research Support, Non-U.S. Gov't

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