Creutzfeldt-Jakob Disease Prediction Using Machine Learning Techniques

Creutzfeldt-Jakob disease (CJD) is a rapidly progressive and fatal neurodegenerative disease, that causes approximately 350 deaths in the United States every year. In specific, it is a prion disease that is caused by a misfolded prion protein, termed PrPSc, which is the infectious form of the prion protein PrPC. Rather than being recycled by the body, the PrPSc aggregates in the brain as plaques, leading to neurodegeneration of surrounding cells and the spongiform characteristics of the pathology. However, there has been very little research done into factors that can affect one's chances of acquiring PrPSc. In this paper, Elastic Net Regression, Long Short-Term Memory Recurrent Neural Network Architectures, and Random Forests have been used to predict Creutzfeldt-Jakob Disease Levels in the United States. Based on common factors that are known to affect CJD, such as soil, food, and water quality, variables were created as data for the models to use. Based on the root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE) values, the study reveals new avenues of research for CJD prevention and detection, as well as potential causes.