摘要
目的建立基于Bayes分类器的肺癌预测模型,探讨并评价该模型的预测效果。方法以前期筛选出的6个噬菌体展示肽与90例肺癌患者血清及90例正常对照血清的反应数据为基础,应用BinReg 2.0软件实现数据分析,建立Bayes肺癌预测模型,并利用受试者工作特征曲线(ROC曲线)评价比较Bayes预测模型与Logistic回归模型、主成分回归模型、支持向量机模型的分类预测效果。结果 Bayes肺癌预测模型的灵敏度为92.00%,特异度为96.00%,能够较好地区分肺癌患者与正常对照。结论 Bayes数学预测模型可较准确地预测受检者患肺癌的概率。
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 effieiencies 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.
出处
《第二军医大学学报》
CAS
CSCD
北大核心
2013年第12期1358-1364,共7页
Academic Journal of Second Military Medical University
基金
上海市登山计划重大课题(06DZ19503)~~
