Ensemble based Data classification for Early Diagnose of Alzheimer

 
   Lead Investigator:    Henok Wordoffa
   Institution      :    Blekinge Institution of Technology
   E-Mail           :    them22dayz@gmail.com
   Proposal ID      :    184


   Publications:


1  Wangoria E, Wordoffa H
   Alzheimer's Disease Stage Prediction using Machine Learning and Multi Agent System
   2013   Dissertation  Thesis, Blekinge Inst of Tech, School of Computing , 

   DEFENDED





   Proposal Description:
   Aims: Machine learning led to significant advances in the fields of medicine  with a     deeper understanding of the classification disease. Alzheimer is a disease  that    is increasing with an alarming rate in the developed country in the elderly    population. This creates the need to find an accurate procedure that can be    made available to healthcare providers for early diagnosis of Alzheimer's    disease. Early detection of Alzheimer is better, before symptoms become  severe    and disruptive to day-to-day functioning. In this paper, we propose to apply    machine learning algorithms on the first two basic preliminary steps of    diagnosing Alzheimer namely medical history and cognitive function test to    diagnose Alzheimer at early stage. We have selected ensemble of classifiers    based on boosting algorithm by combine different classifiers with the  different   data set of medical history and cognitive function test in order to increase    classification accuracy of early diagnosis of Alzheimer.