期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 196, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2020.105686
关键词
Bayesian network; Multiple primary cancer; Survivability
类别
资金
- Ministry of Science and Technology and Ministry of Education, R.O.C. (Taiwan) [MOST 105-2221-E-011 -10G -MY2]
Background and Objective: Multiple primary cancers significantly threat patient survivability. Predicting the survivability of patients with two cancers is challenging because its stochastic pattern relates with numerous variables. Methods: In this study, a Bayesian network (BN) model was proposed to describe the occurrence of two primary cancers and predict the five-year survivability of patients using probabilistic evidence. Eleven types of major primary cancers and contingent occurrences of secondary cancers were investigated. A nationwide two-cancer database involving 7,845 patients in Taiwan was investigated. The BN topology is rigorously examined and imbalanced dataset is processed by the synthetic minority oversampling technique. The proposed BN survivability prognosis model was compared with benchmark approaches. Results: The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naive Bayes in terms of sensitivity, which is a critical performance index for the non-survival group. Conclusions: Using the proposed BN model, one can estimate the posterior probabilities for every query provided appropriate prior evidences. The potential survivability information of patients, treatment effects, and socio-demographics factor effects predicted by the proposed model can help in cancer treatment assessment and cancer development monitoring. (C) 2020 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据