Modeling Lung Cancer Diagnosis using Bayesian Network Inference

Although current low-dose CT screening program schemes provide enormous amounts of screening data, these schemes nevertheless encounter challenges because of high rates of over-diagnosis, high costs, and increased radiation exposure. To reduce false positive results and improve the effectiveness of screening program schemes, these challenges call for automated lung cancer detection and diagnostic technology. Bayesian networks (BNs), a machine-learning method for knowledge representation and risk estimate in the field of medicine, are introduced here. It explains how BNs are concise and understandable graphical representations of joint probability distributions (JPDs) that may be used for causal reasoning and risk estimation analyses and have various advantages over regression-based techniques. It highlights how BNs are graphical representations of JPDs that take the form of a network representing model random variables and the influences between them, respectively. BNs are a different approach to risk estimation, and they are a network representing model random variables and the influences between them. The proposed paper examines some of the issues with conventional risk prediction techniques before describing BNs, their development, use and benefits in risk prediction using examples from cancer and heart disease. Here there are three major health outcome researches is focused on the first, generation of network structures in which relationships between variables can be easily communicated. Second is their ability to apply Bayes theorem to conduct individual-level risk estimation, and the third is their straightforward conversion into decision models. Risk modeling with BNs has advantages over regression-based approaches. In the era of precision medicine, Bayesian networks constitute a potent and adaptable tool for the analysis of health economics and outcomes research data.