title: Artificial neural network may perform good to predict the survivability of cervical cancer.
abstract: The project demonstrated to analyze the survivability of cervical cancer from the large data set of SEER (Surveillance Epidemiology and End Results). The data were re-sampled into 10 folds on 5 different size that were based on for three methods- artificial neural network, logistic regression and decision tree- to establish models for predicting the survivability of cervical cancer. In the meanwhile, 10-fold cross-validation was used to examine the respective models of three methods for performance comparison.
authors.affiliation: School of Public Health, National Defense Medical College, Taipei, Taiwan.
authors.forename: Chi-Ming
authors.initials: CM
authors.lastname: Chu
authors.forename: Yu-Tian
authors.initials: YT
authors.lastname: Chang
authors.forename: Thomas
authors.initials: T
authors.lastname: Wetter
mesh_terms: D019540:Area Under Curve; D003663:Decision Trees; D005260:Female; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer; D018426:SEER Program; D016019:Survival Analysis; D002583:Uterine Cervical Neoplasms / Q000401:mortality*