2005 Research Fair Archive - Physics Abstracts
Predicting the Outcome of Kidney Transplants
In 1988, the United States Renal Data System (USRDS) began operations by the National Institute of Diabetes and Digestive and Kidney Diseases in conjunction with the Health care financing administration. They constructed a data set that contains information on every patient that received a kidney transplant in the United States from January 1st, 1990. There is a shortage of kidneys available for transplant; in 2001, only 27% of those patients waiting for a kidney received a transplant. Thus, it is important to identify those patients who are likely to reject a transplant. Physicians model their patients with logistic regression to predict the outcome of future transplants. These models compare a patient’s features, like age, hypertension, diabetes, with those mined from the data base. However, logistic regression lacks the ability to model complex non-linear interactions among the features that make up a patients model. Artificial Neural Networks (ANN) and Radial Basis Functions (RBF) are data mining techniques with the capacity of modeling complex, non-linear (and linear) data sets. However, they are not widely used to mine medical databases. RBFs are linear combinations of radially symmetric nonlinear basis functions. Our principle hypothesis was that non-linear modeling techniques, such as ANNs and RBFs, we would obtain better predictions than logistic regression. Trained RBF networks with approximately 50,000 nodes were used to predict the survival of kidney transplants. In fact, RBFs outperformed logistic regression in terms of predicting survival.