Building a Graph-Based Bayesian Network

A team of researchers is investigating the causes of lung cancer. They have identified various risk factors and potential causal relationships between these factors and the development of lung cancer. Their findings include:

Smoking, age, family history of lung cancer, exposure to asbestos, air pollution exposure, radon exposure, lung infections, body mass index, and passive smoking are all considered risk factors for lung cancer.

• Environmental risk: Exposure to asbestos, air pollution, and radon gas can all contribute to an increased risk for lung cancer.

• Infection risk: A history of lung infections and exposure to passive smoking can contribute to a higher risk for lung cancer.

• Genetic risk: A family history of lung cancer and age are both factors that can contribute to an increased risk for lung cancer.

• Lifestyle risk: Smoking and body mass index are both lifestyle factors that can contribute to an increased risk for lung cancer.

Using these causal relationships, you can create a Bayesian network step by step. This project will guide you through the following tasks:

• Define the input nodes based on the risk factors.

• Create synthetic nodes for environmental risk, infection risk, genetic risk, and lifestyle risk, and connect the input nodes to them accordingly.

• Define the target node for lung cancer and connect the synthetic nodes to it.

• Implement the Bayesian network in Python using the CausalNex library.

• Fit the model and perform predictions.

This project will equip you to understand causal relationships and how to create Bayesian networks using CausalNex in Python.