title: [Diagnosis and prediction of lung cancer through different classification techniques with tumor markers].
abstract: OBJECTIVE
  To study which classification model was most suitable for establishing a multi-tumor markers lung cancer prediction model, through established logistic regression model, decision trees model and artificial neural network model.
  
  
  METHODS
  RIA analysis, ELISA, spectrophotometry, high-performance liquid chromatography (HPLC) and atomic absorption spectrometry were used to measure the serum CEA, CA125, gastrin, NSE, beta2-MG, Sil-6 receptors, sialic acid, nitric oxide, Cu, Zn, Ca and the pseudo-urine nucleoside of urine samples in lung cancer patients, benign lung disease patients and healthy controls. The lung cancer diagnosis models were established by logistic regression analysis, decision tree analysis and artificial neural network training.
  
  
  RESULTS
  The diagnosis sensitivities of the logistic regression analysis, decision tree analysis and artificial neural network model with 12 tumor markers in lung cancer were 94.00%, 100.00% and 100.00%; the specificity were 100.00%, 98.89% and 100.00%; the total accurate 94.29%, 95.00% and 90.00%, respectively.
  
  
  CONCLUSIONS
  The results of three classification models with 12 tumor markers in diagnosis of lung cancer are ideal. Especially the C5.0 decision tree model and the artificial neural network model are more suitable for the prediction and diagnosis of the lung cancer.
authors.affiliation: College of Public Health, Zhengzhou University, Zhengzhou 450001, China.
authors.forename: Guang-jin
authors.initials: GJ
authors.lastname: Nie
authors.forename: Fei-fei
authors.initials: FF
authors.lastname: Feng
authors.forename: Yong-jun
authors.initials: YJ
authors.lastname: Wu
authors.forename: Yi-ming
authors.initials: YM
authors.lastname: Wu
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000032:analysis; D003663:Decision Trees; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer
chemical_list: D014408:Biomarkers, Tumor