\n\nLead in drinking water is a serious public health issue. Even small amounts can cause neurological damage. Our research set out to find better ways to predict lead risk in private water systems. We used something called Bayesian Network approaches to look at how different things interact: household details, geology, what people see in their tap water, and lab test results.\n\nWe built a framework for finding knowledge by combining methods for sorting data, picking out key features, and using Bayes classifiers. We tried both “forward selection” and “backward selection” to pick features. For continuous data, we tested different ways to group it, including using what we already know, statistical methods, and information-based approaches. We also tested a few Bayes classifiers, like General Bayesian Network, Naive Bayes, and Tree-Augmented Naive Bayes, to help us identify how things are connected. Then, we used Bayesian inference to fill in the probability tables for each connection.\n\nWe applied this Bayesian system to a dataset from the Virginia Household Water Quality Program (VAHWQP). They collected water samples and surveyed 2,146 households with private water systems, like wells and springs, in Virginia during 2012 and 2013. We looked at how lab-tested water quality, tap water observations, and household features, such as plumbing type, water source, location, and on-site water treatment, all connect to predict lead levels. \n\nOur results show that Naive Bayes classifiers work best for recall and precision compared to other classifiers. Copper is the most important predictor of lead. Other big predictors include the county, pH, and whether there’s on-site water treatment. The way we picked features didn’t change performance much, but how we grouped the data really affected how well the models worked with the classifiers.\n\nOwners of private wells are still at a disadvantage and might face higher risks. Why? Because utilities and government agencies aren’t responsible for making sure lead levels in private wells meet the Lead and Copper Rule. The insights we gained from these models can help us pinpoint which water quality factors, plumbing types, and household details increase the chance of high lead levels. This information is crucial for making smart decisions about lead testing and treatment.\n\n