title: Probabilistic modeling of short survivability in patients with brain metastasis from lung cancer.
abstract: The prediction of substantially short survivability in patients is extremely risky. In this study, we proposed a probabilistic model using Bayesian network (BN) to predict the short survivability of patients with brain metastasis from lung cancer. A nationwide cancer patient database from 1996 to 2010 in Taiwan was used. The cohort consisted of 438 patients with brain metastasis from lung cancer. We utilized synthetic minority over-sampling technique (SMOTE) to solve the imbalanced property embedded in the problem. The proposed BN was compared with three competitive models, namely, naive Bayes (NB), logistic regression (LR), and support vector machine (SVM). Statistical analysis showed that performances of BN, LR, NB, and SVM were statistically the same in terms of all indices with low sensitivity when these models were applied on an imbalanced data set. Results also showed that SMOTE can improve the performance of the four models in terms of sensitivity, while keeping high accuracy and specificity. Further, the proposed BN is more effective as compared with NB, LR, and SVM from two perspectives: the transparency and ability to show the relation of factors affecting brain metastasis from lung cancer; it allows decision makers to find the probability despite incomplete evidence and information; and the sensitivity of the proposed BN is the highest among all standard machine learning methods.
authors: Faculty of Commerce and Management, Prince of Songkla University, Trang, Thailand. Electronic address: bunjira_m@hotmail.com.
authors: Bunjira
authors: B
authors: Makond
authors: Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan, ROC. Electronic address: kjwang@mail.ntust.edu.tw.
authors: Kung-Jeng
authors: KJ
authors: Wang
authors: Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan, ROC. Electronic address: albert.hua@msa.hinet.net.
authors: Kung-Min
authors: KM
authors: Wang
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000401:mortality / Q000556:secondary*; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D011379:Prognosis; D060388:Support Vector Machine; D016019:Survival Analysis; D013624:Taiwan / Q000453:epidemiology
keywords: Bayesian network; Brain metastasis; Lung cancer; Survivability