It is very important to inform the patients and their relatives about the risk of mortality before a cardiovascular operation. For this respect, a model called EuroSCORE (The European System for Cardiac Operative Risk Evaluation) has been developed by European cardiovascular surgeons. This system gives the risk of mortality during or 30 days after the operation, based on the values of some parameters measured before the operation. The model used by EuroSCORE has been developed by statistical data gathered from large number of operations performed in Europe. Even though due to the surgical techniques that have been developed recently and the risk of mortality has been reduced in a large extent, predicting that risk as accurately as possible is still primary concern for the patients and their relatives in cardiovascular operations. The risk of operation also essentially tells the surgeon how a patient with similar comorbidity would be expected to fare based on a standard care. The risk of patient is also important for the health insurance companies, both public or private. In the context of this project, a model that can be used for mortality is developed. In this research project, a database system for storing data about cardiovascular operations performed in Turkish hospitals, a web application for gathering data, and a machine learning system on this database to learn a risk model, similar to EuroSCORE, are developed. This thesis proposes a risk estimation system for predicting the risk of mortality in patients undergoing cardiovascular operations by maximizing the Area under the Receiver Operating Characteristic (ROC) Curve (AUC). When the genetic characteristics and life styles of Turkish patients are taken into consideration, it is highly probable that the mortality risks of Turkish patients may be different than European patients. This thesis also intends to investigate this issue.