As a form of knowledge representation and supervised machine learning, Bayesian networks have become increasingly popular because of their efficient inference and their inherent ability of intuitive graphical representation of causal interactions among covariates. For these reasons they are often used in decision support systems in various fields of medicine. At the same time they are being almost completely ignored in survival analysis. This is because special data-handling techniques are needed for handling survival data, due to the bad influence of censoring. This thesis deals with the problem of learning Bayesian networks from censored survival data. Several known data-preprocessing techniques for machine learning and a new technique are presented. Models used include two well-known algorithms for learning Bayesian networks, the naive Bayes classifier and the proportional hazards regression model. Thorough testing was conducted on both a simulation study and on several real-world domains from the field of clinical medicine. Assessment of the efficiency of the data-preprocessing techniques on models was conducted using statistical comparison on test results of several standard machine-learning and survival-analysis metrics. The Bayesian network learning algorithms' ability of identifying the correct causal structure after using different data-preprocessing techniques was also tested. Simulation study results suggest that the proposed data-preprocessing technique of reducing censoring noise performs excellently in heavily censored domains.