Application of bayesian classifier tn diagnosis of lung cancer by multiple autoantibody biomarkers / 第二军医大学学报
Academic Journal of Second Military Medical University
;
(12): 1358-1363, 2013.
Article
in Chinese
| WPRIM
| ID: wpr-839316
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 6 phage peptide clones with the sera of 90 lung cancer patients and 90 healthy controls, we established a Bayesian classifier-based lung cancer prediction model, with the data analyzed by BinReg 2.0 software. 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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Type of study:
Prognostic study
Language:
Chinese
Journal:
Academic Journal of Second Military Medical University
Year:
2013
Type:
Article
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