Naive Bayes models for probability estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
Full Bayesian network classifiers
ICML '06 Proceedings of the 23rd international conference on Machine learning
Diagnosis of breast cancer using Bayesian networks: A case study
Computers in Biology and Medicine
Selection of human embryos for transfer by Bayesian classifiers
Computers in Biology and Medicine
Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions
Artificial Intelligence in Medicine
Comparing data mining methods with logistic regression in childhood obesity prediction
Information Systems Frontiers
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Comparison of machine learning methods for intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
A Bayesian network model for predicting pregnancy after in vitro fertilization
Computers in Biology and Medicine
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The Bayesian network (BN) is a promising method for modeling cancer metastasis under uncertainty. BN is graphically represented using bioinformatics variables and can be used to support an informative medical decision/observation by using probabilistic reasoning. In this study, we propose such a BN to describe and predict the occurrence of brain metastasis from lung cancer. A nationwide database containing more than 50,000 cases of cancer patients from 1996 to 2010 in Taiwan was used in this study. The BN topology for studying brain metastasis from lung cancer was rigorously examined by domain experts/doctors. We used three statistical measures, namely, the accuracy, sensitivity, and specificity, to evaluate the performances of the proposed BN model and to compare it with three competitive approaches, namely, naive Bayes (NB), logistic regression (LR) and support vector machine (SVM). Experimental results show that no significant differences are observed in accuracy or specificity among the four models, while the proposed BN outperforms the others in terms of sampled average sensitivity. Moreover the proposed BN has advantages compared with the other approaches in interpreting how brain metastasis develops from lung cancer. It is shown to be easily understood by physicians, to be efficient in modeling non-linear situations, capable of solving stochastic medical problems, and handling situations wherein information are missing in the context of the occurrence of brain metastasis from lung cancer.