Predicting Alzheimers Disease Diagnosis Risk over Time with Survival
  Machine Learning on the ADNI Cohort
Henry Musto Daniel Stamate Ida Pu Daniel Stahl
The rise of Alzheimers Disease worldwide has prompted a search for efficient
tools which can be used to predict deterioration in cognitive decline leading
to dementia. In this paper, we explore the potential of survival machine
learning as such a tool for building models capable of predicting not only
deterioration but also the likely time to deterioration. We demonstrate good
predictive ability (0.86 C-Index), lending support to its use in clinical
investigation and prediction of Alzheimers Disease risk.