[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).


METHODS
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.


RESULTS
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%.


CONCLUSIONS
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.
Shen
Yu
Y
Dept. of Bio-Medical Engineering, Shanghai Jiaotong University, Shanghai, China.
Zhuang
Tian-ge
TG
Cheng
Hong-yan
HY
Xu
Wen
W
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016461:Chemoembolization, Therapeutic; D006801:Humans; D008113:Liver Neoplasms / Q000628:therapy*; D011379:Prognosis