[Diagnosis and prediction of lung cancer through different classification techniques with tumor markers].
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.
Nie
Guang-jin
GJ
College of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Feng
Fei-fei
FF
Wu
Yong-jun
YJ
Wu
Yi-ming
YM
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
D014408:Biomarkers, Tumor