Uncontrolled and aberrant cell proliferation in lung tissues is the cause of lung cancer, one of the most prevalent and deadly malignancies in the world. The LASSO approach was initially used in this study to find factors that might be connected to the disease's start. After that, a Bayesian logistic regression model was built, and in order to get more accurate parameter estimates, extensive posterior chains were generated using MCMC sampling. 
To ascertain which variables in the Bayesian model had the greatest impact on the occurrence of disease, their relative relevance was assessed using 95% Confidence Intervals and Posterior Means. With the exception of one variable that it kept but the Bayesian analysis subsequently eliminated due to its credible interval, LASSO chose a comparable set of variables. 
Overall, the two approaches yielded almost equal sets of significant variables; nevertheless, the Bayesian logistic regression model performed better. It offered more accurate explanations of the variables influencing the incidence of lung cancer as well as a clearer assessment of parameter significance.