Abstract
Objective To establish a Bayesian classifier-based lung cancer prediction model,and to discuss its predictive efficiency.Methods Using the reaction data of previously screened 6phage peptide clones with the sera of 90lung cancer patients and 90healthy controls,we established a Bayesian classifier-based lung cancer prediction model,with the data analyzed by BinReg 2.0software.The predictive efficiencies of different models(Bayesian classifier-based prediction model, Logistic regression,principal component regression,and support vector machine)were evaluated by receiver operating characteristic(ROC)curves.Results The sensitivity and specificity of Bayesian classifier-based lung cancer prediction model were 92.00%and 96.00%,respectively.And the model satisfactorily distinguished lung cancer patients and healthy controls. Conclusion Our Bayesian classifier-based lung cancer prediction model can accurately predict the risk of lung cancer.
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