Prediction of Amyloid positivity using machine learning methods

 
   Lead Investigator:    Guogen Shan
   Institution      :    University of Nevada, Las Vegas
   E-Mail           :    guogen.shan@unlv.edu
   Proposal ID      :    1488



   Proposal Description:
   In addition to age, ADAS-cog 13, and ApOE status, other importance factors could increase the prediction accuracy of amyloid, such as smoking status, sleeping. We will use the NACC data to investigate the prediction of amyloid positivity by using neuropathology data, along with the demographic data, and genetic data including Apoe status. The second aim is to see whether brain volume data would increase the prediction power. The last aim is to use longitudinal data to predict "when" the amyloid status changes