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[Comparison and analysis of three classifying models for discrimination of lung cancer established by 6 tumor
Feifei Feng1, Guangjin Nie, Yongjun Wu
1College of Public Health, Zhengzhou University, Zhengzhou 450001, China. heartnessff@126.com
Objective:
To distinguish lung cancer by detecting 6 tumor markers in serum and establishing three classifying models of artificial neural networks (ANN), decision tree (CART), Fisher discrimination analysis, and to compare the differences among three models.
Methods:
The levels of serum CEA, gastrin, NSE, sialic acid (SA), Cu/ Zn, Ca in 50 healthy individuals, 40 patients with lung benign disease and 50 patients with lung cancers were detected by means of radioimmunology, spectrophotometry, atomic absorption spectrophotometry, respectively, and then developed ANN, CART and Fisher discrimination analysis models.
Results:
The sensitivity of ANN, CART and Fisher discrimination analysis models were 100%, 93.33%, 84.00%, the specificity were 100%, 100%, 98.89%, the accuracy were 91.67%, 86.11%, 85.00%. The areas under receiver operating curve (AUROC) of ANN, CART and Fisher discrimination analysis models were 0.964, 0.953, 0.812, respectively. There was no significantly statistical difference between ANN and CART (P > 0.05), while there were significantly statistical differences not only between Fisher discrimination analysis and ANN, but also Fisher discrimination analysis and CART (P < 0.05).
Conclusion:
The effects of ANN, CART models established by 6 tumor markers were better than that of Fisher discrimination analysis in discrimination of lung cancer.