Survivability prognosis of lung cancer patients with comorbidities-a Gaussian Bayesian network model.

Abstract

Comorbidities are influencing factors that cause lung cancer. An accurate survivability prediction model is required considering these confounding factors (a variety of comorbidities and treatments). The study developed a conditional Gaussian Bayesian network (CGBN) model to predict the related survival time with likelihood under various conditions. The lung cancer patients were collected from the National Health Insurance Research Database in Taiwan. Six major chronic diseases (i.e., pulmonary tuberculosis, COPD, kidney failure, diabetes mellitus, stroke, and liver disease) are investigated. A total of 2875 lung cancer cases with key comorbidities were selected. This study examined three types of lung cancer treatment: surgery, chemotherapy, and targeted therapy. The study outcomes provided the likelihood of survival time occurrences. Survival analysis indicates that diabetes mellitus and liver disease are significantly riskier than the other comorbidities for lung cancer patients. The proposed CGBN model achieved high accuracy as compared to the existing literature. The proposed CGBN model is advantageous for modeling the relationship between numerical and categorical influencing factors and response variables for lung cancer with comorbidities. The proposed model facilitates the flexible and accurate estimation of various lung cancer-related queries.