Prediction of cerebral aneurysm rupture

Abstract

Cerebral aneurysms are weak or thin spots on blood vessels in the brain that balloon out. While the majority of aneurysms do not burst, those that do would lead to serious complications including hemorrhagic stroke, permanent nerve damage, or death. Yet, surgical options for treating 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 application, 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 discrete 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 systematic approach to build such a classifier through suitable combination of data mining algorithms. Our approach automatically determines the optimal combination of these algorithms for a dataset. The system has an accuracy of 92% and is currently being deployed at the Huashan Hospital. © 2007 IEEE.