The application of ElasticNet and XGboost model in the Alzheimer's disease and
dementia with Lewy body 


   Note: An Official Proposal has not been Submitted to NACC.


   Lead Investigator:    Fuliang Yi
   Institution      :    Shanxi Medical Unversity
   E-Mail           :    18783934308@163.com
   Proposal ID      :    1721



   Proposal Description:
   ElasticNet model stands out in feature selection and XGboost, a tree-based model, enjoy better performance in classification and prediction. Since Alzheimer's disease(AD) and dementia with Lewy body(DLB) are similar to each other in clinical manisfestion, it is difficult to differentiate them. It has been well tested that XGboost is great in differentiating Major depressive disorder (MDD) and Bipolar disorder (BD) in terms of accuracy, sensitivity, specificity. In the present study, we aim to make a classification over AD and DLB by XGboost model with demographic information, clinical and imaging data. We also aim to make a comparison with widely-used supportive vector machine(SVM), with hope to supplement clinical diognosis of these two diseases.