Title: Constructing Continuous Bayesian Networks with GOMEA
Abstract: For years, the field of artificial intelligence (AI) has seen significant growth, with neural networks being a popular choice for predictive modeling due to their high accuracy. However, in healthcare there is a reluctance to widely adopt these AI models as it is difficult, if not impossible to interpret the underlying mechanics of such models. To address this issue on interpretability, interpretable machine learning techniques such as Bayesian networks can be used. However, Bayesian networks come at the cost of being less accurate. To enhance the predictive power of Bayesian networks while keeping the model interpretable, an existing Bayesian network search algorithm has been extended to handle continuous data in an interpretable way.
