Shen Yu, Zhuang Tian-ge, Cheng Hong-yan, Xu Wen
Dept. of Bio-Medical Engineering, Shanghai Jiaotong University, Shanghai, China.
Space Med Med Eng (Beijing). 2004 Oct;17(5):350-4.
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).
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.
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%.
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.
将朴素贝叶斯算法应用于原发性肝癌的预后预测，并预测经动脉化疗栓塞术（TACE）后患者的生存预期。
应用朴素贝叶斯算法。采用相关性分析筛选数据属性。假定缺失数据与已知数据遵循相同分布。
缺失数据的同分布假设将错误率从71.9%降低至9.4%。通过相关性分析从39个属性中筛选出12个属性，这些属性对最终分类更有效，且错误率相对较低，为3.1%。
所提方法有效提高了分类准确率。朴素贝叶斯算法在原发性肝癌预后问题中的成功应用表明该方法在医学领域具有广阔前景。
