Imputation of SEER Data using Artificial Neural Networks

This project was presented as a part of the CISNET Junior Investigator series. The goal of this work was to use neural networks to remedy known missing data issues in a series of SEER databases. The Multilayer Perceptron model was an excellent candidate for imputation over this dataset due to the high dimensionality of underlying data. Our results were later integrated into certain CISNET models.