朴素贝叶斯算法在原发性肝癌预后预测中的研究  

Discussion of Naive Bayesian Algorithm in Prognosis Prediction of Primary Liver Cancer.

作　　者：申羽[1] 庄天戈[1] 程红岩[2] 徐雯[2] 

机构地区：[1]上海交通大学生物医学工程系,上海200030 [2]第二军医大学东方肝胆外科医院放射科,上海200438

出　　处：《航天医学与医学工程》2004年第5期350-354,共5页Space Medicine & Medical Engineering

摘　　要：目的研究贝叶斯算法在原发性肝癌预后预测中的应用 ,预测病人动脉化疗栓塞术 (TACE)后生存期分类。方法对实际可用的 1 92例原发性肝癌临床病例进行研究 ,根据数据属性与预测目标的相关程度大小筛选属性 ,剔除弱相关属性 ;并以同概率分布假设对数据样本中的缺损数据进行预处理 ,应用贝叶斯算法对病人TACE术后生存期进行预测分类。结果对缺损数据的同概率分布假设可以使分类错误率由 71 .9%下降到 9.4% ;属性相关程度分析从 39个病理属性中筛选得到 1 2个对原发性肝癌预后预测贡献大的属性 ,得到较小分类错误率 3.1 %。结论本项研究所使用的数据预处理方法提高了原发性肝癌预后生存期预测分类准确率 。Objective To apply naive Bayesian algorithm in prognosis prediction of primary liver cancer and to predict the survival expectation of patients after transcatheter arterial chemoembolization(TACE). Method Naive Bayesian algorithm was applied .Using correlation analysis to sift data-attributes. Whereas the missing data were assumed to follow the same distribution as that of the known. Result The same-distribution assumption of the missing data reduces the error rate from 71.9% to 9.4%. Twelve attributes were sifted from 39 attributes by the correlation analysis , which were more effective to the final classification , and had a relatively low error rate of 3.1%. Conclusion The proposed method effectively increases the accuracy of classification .Successful application of the naive Bayesian algorithm in prognostic problem of primary liver cancer indicates a bright future of this method in medical field.

关 键 词：数据挖掘 朴素贝叶斯分析 预后 肝癌 数据预处理 

分 类 号：R319[医药卫生—基础医学]