title: Diagnosing breast cancer based on support vector machines.
abstract: The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.
authors.affiliation: Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
authors.forename: H X
authors.initials: HX
authors.lastname: Liu
authors.forename: R S
authors.initials: RS
authors.lastname: Zhang
authors.forename: F
authors.initials: F
authors.lastname: Luan
authors.forename: X J
authors.initials: XJ
authors.lastname: Yao
authors.forename: M C
authors.initials: MC
authors.lastname: Liu
authors.forename: Z D
authors.initials: ZD
authors.lastname: Hu
authors.forename: B T
authors.initials: BT
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D016000:Cluster Analysis; D019295:Computational Biology; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer