| 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。 |
| 关键词: 肺肿瘤 预测模型 Bayes分类器 生物学肿瘤标记 |
| DOI： |
| 投稿时间：2013-02-27修订日期：2013-07-19 |
| 基金项目:上海市登山计划重大课题(06DZ19503). |
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| Application of Bayesian classifier in diagnosis of lung cancer by multiple autoantibody biomarkers |
| LIU Yan,CHANG Wen-jun,CAO Guang-wen* |
(Department of Epidemiology, School of Tropical Medicine and Public Health, Second Military Medical University, Shanghai 200433, China
*Corresponding author.) |
| 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. |
| Key words: lung neoplasms prediction model Bayes classifier biological tumor markers |
