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. CONCLUSION: 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.
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 cancerpatients, benign lung diseasepatients 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. CONCLUSION: 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.