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

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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 R2 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.

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The authors gratefully acknowledge the comments and suggestions of the editor and the anonymous referees.

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Correspondence to Kung-Jeng Wang.

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This work is partially supported by Ministry of Science & Technology of the Republic of China (Taiwan) under the grant no. MOST 105–2221-E-011-106 -MY2.

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All the authors declare that he/she has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Wang, K., Chen, J., Chen, K. et al. Survivability Prognosis for Lung Cancer Patients at Different Severity Stages by a Risk Factor-Based Bayesian Network Modeling. J Med Syst 44, 65 (2020).

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  • Bayesian network
  • Lung cancer
  • Risk adjustment factor
  • Survivability