Survivability Prognosis for Lung Cancer Patients at Different Severity Stages by a Risk Factor-Based Bayesian Network Modeling

Lung cancer is a major reason of mortalities. Estimating the survivability for this disease has become a key issue to families, hospitals, and countries. A conditional Gaussian Bayesian network model was presented in this study. This model considered 15 risk factors to predict the survivability of a...

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Bibliographic Details
Published in:Journal of medical systems 2020-03, Vol.44 (3), p.65-65, Article 65
Main Authors: Wang, Kung-Jeng, Chen, Jyun-Lin, Chen, Kun-Huang, Wang, Kung-Min
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Cancer Survivors - statistics & numerical data
Databases, Factual - statistics & numerical data
Demographic variables
Female
Health Informatics
Health Sciences
Humans
Lung cancer
Lung Neoplasms - mortality
Male
Mathematical models
Medical prognosis
Medicine
Medicine & Public Health
Model accuracy
Models, Biological
Patient Facing Systems
Prognosis
Risk analysis
Risk factors
Severity of Illness Index
Statistics for Life Sciences
Survivability
Survival Analysis
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Summary:Lung cancer is a major reason of mortalities. Estimating the survivability for this disease has become a key issue to families, hospitals, and countries. A conditional Gaussian Bayesian network model was presented in this study. This model considered 15 risk factors to predict the survivability of a lung cancer patient at 4 severity stages. We surveyed 1075 patients. The presented model is constructed by using the demographic, diagnosed-based, and prior-utilization variables. The proposed model for the survivability prognosis at different four stages performed R 2 of 93.57%, 86.83%, 67.22%, and 52.94%, respectively. The model predicted the lung cancer survivability with high accuracy compared with the reported models. Our model also shows that it reached the ceiling of an ideal Bayesian network.
ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-020-1537-5