Cerebral aneurysms are weak or thin spots on blood ves- sels in the brain that balloon out. While the majority of aneurysms do not burst, those that do would lead to se- rious complications including hemorrhagic stroke, perma- nent nerve damage, or death. Yet, surgical options for treat- ing cerebral aneurysms carry high risk to the patient. It is vital for the doctors to accurately diagnose aneurysms that have high probabilities of rupturing. In this applica- tion, the patient dataset has many attributes, ranging from patient profile to results from diagnostic test and features extracted from brain images. Many of the attributes are dis- crete and have missing values. The dataset is also highly biased, with 15% unrupture cases and 85% rupture cases. Building a classifier that unerringly predicts the unrupture (rare) class is a challenge. In this paper, we describe a sys- tematic approach to build such a classifier through suitable combination of data mining algorithms. Our approach au- tomatically determines the optimal combination of these al- gorithms for a dataset. The system has an accuracy of 92% and is currently being deployed at the Huashan Hospital.