朴素贝叶斯算法在原发性肝癌预后预测中的研究

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192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12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，证明本项研究在医学领域将有着广泛的应用前景.

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

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.