Survivability modelling using Bayesian network for patients with first and secondary primary cancers. (November 2020)

• Record Type: Journal Article
• Authors: Wang, Kung-Min; Wang, Kung-Jeng; Makond, Bunjira
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Bayesian network -- Multiple primary cancer -- Survivability; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105686
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlight: A novel Bayesian network (BN) model is proposed to describe the occurrence of two primary cancers and predict the five-year survivability. Eleven types of major primary cancers and contingent occurrences of secondary cancers are investigated. The nationwide two-cancer database involving 7, 845 patients in Taiwan is investigated. The proposed BN survivability prognosis model significantly outperforms the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity. Abstract: 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 naïve 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.