… of sparse regression to make more excellent prediction results. Finally, we classified …
selected subjects with s-MRI data at four longitudinal time points without conversion, baseline …

… In this context, reliably predicting conversion of MCI to AD can … The most widely used
embedded methods are sparse learning … Alternatively to error minimization, stability selection [21] …

… learning-based method for the prediction of MCI-to-AD conversion within 3 years, by combining
baseline (ie… , and APOe4 genetic data from the ADNI database. We achieved a very high …

… -tuning procedure for the MCI conversion prediction task was obtained from ADNI. All subjects
… These subjects were diagnosed as MCI during the baseline visit. Among the 297 subjects, …

… obtained from the ADNI database (www.loni.ucla.edu/ADNI). … The standardized list of baseline
scans from ADNI-1 [32] was … a sparse representation of each MCI subject, we also use the …

… Initiative (ADNI) database (adni.loni.usc.edu). The … we applied the sparse linear regression
with the stability selection to … insight into the prediction of MCI to AD conversion, and revealed …

… sparse regression method to fuse the auxiliary data into the … data of Alzheimer’s Disease
Neuroimaging Initiative (ADNI) … to identify whether a MCI subject progresses to AD (ie, pMCI) …

… use of Jacobian Determinant (JD) from the ADNI baseline … the conversion of progressive
MCI people from stable people. … solving the problem of predicting the time-to-AD class of these …

… Specifically, using a set of features derived from the ADNI … sparse set of features with minimal
within-correlation and maximal … in predicting clinical progression from known baseline data. …

… on the ADNI cohort, we performed both binary and multi-class classification tasks in AD/MCI …
By regarding the prediction of each target vector \({\mathbf{y}}^{i}\) (\(i\in \{1,\ldots ,C\}\)) as a …