PREDICTING CARDIOVASCULAR RISKS - Using POSSUM, PPOSSUM and Neural Net Techniques

Abstract

Abstract:Neural Networks are broadly applied in a number of fields such as cognitive science, diagnosis, and forecasting. Medical decision support is one area of increasing research interest. Ongoing collaborations between cardiovascular clinicians and computer science are looking at the application of neural networks (and other data mining techniques) to the area of individual patient diagnosis, based on clinical records (from Hull and Dundee sites). The current research looks to advance initial investigations in a number of ways. Firstly, through a rigorous analysis of the clinical data, using data mining and statistical tools, we hope to be able to extend the usefulness of much of the clinical data set. Problems with the data include differences in attribute presence and use across different sites, and missing values. Secondly we look to advance the classification of referred patients with different outcome through the rigorous use of POSSUM, PPOSSUM and both supervised and unsupervised neural net techniques. Through the use of different classifiers, a better clinical diagnostic support model may be built.