• Login
    View Item 
    •   Athenaeum Home
    • University of Georgia Theses and Dissertations
    • University of Georgia Theses and Dissertations
    • View Item
    •   Athenaeum Home
    • University of Georgia Theses and Dissertations
    • University of Georgia Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Automated MRI prediction of Alzheimer's disease development by machine learning methods

    Thumbnail
    Date
    2011-12
    Author
    Meng, Meng
    Metadata
    Show full item record
    Abstract
    Alzheimer's is an irreversible brain disease that impairs memory, thinking and behavior and leads ultimately to death.Research has shown that individuals with MCI (mild cognitive impairment), the pre-stage of Alzheimer's, have an increased risk of developing Alzheimer's over the next few years . It is useful and important to diagnose and predict MCI's conversion to Alzheimer's as early as possible for appropriate treatment. In our study, we use numerous machine learning, feature selection as well as clustering methods for this prediction purpose. High precision of prediction is observed for both 10-fold and 2-fold cross-validation. We also use L1 and L2-norm shrinkage terms to control the model complexity. As a result, the prediction error is reduced. These findings illustrate that machine learning methods accurately and reliably predict MCI's conversion, and potentially provide a great assistance to medical diagnosis.
    URI
    http://purl.galileo.usg.edu/uga_etd/meng_meng_201112_ms
    http://hdl.handle.net/10724/27767
    Collections
    • University of Georgia Theses and Dissertations

    About Athenaeum | Contact Us | Send Feedback
     

     

    Browse

    All of AthenaeumCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    About Athenaeum | Contact Us | Send Feedback