Crossmark

Machine Learning Workflow to Explain Black-Box Models for Early Alzheimer’s Disease Classification Evaluated for Multiple Datasets

Crossref DOI link: https://doi.org/10.1007/s42979-022-01371-y

Published Online: 2022-10-06

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Bloch, Louise https://orcid.org/0000-0001-7540-4980
Friedrich, Christoph M. https://orcid.org/0000-0001-7906-0038
,
Funding

Funding for this research was provided by:

PhD grant from University of Applied Sciences and Arts Dortmund, Germany

Fachhochschule Dortmund
License Information

Text and Data Mining valid from 2022-10-06

Version of Record valid from 2022-10-06
More Information

Article History

Received: 11 October 2021

Accepted: 14 August 2022

First Online: 6 October 2022

Declarations

:

: The authors declare that they have no competing interests.

: Not applicable.

: The ADNI study was approved by the institutional review boards of the participating institutions. All participants gave informed written consent. More details can be found online (ADNI: , Accessed: 2022-05-01). The AIBL study was approved by the institutional ethics committees of Austin Health, StVincent’s Health, Hollywood Private Hospital and Edith Cowan University. All participants gave written informed consent before participating in the study. All OASIS participants consented to Knight Ageing and Disability Resource Center (ADRC)-related projects following procedures approved by the Institutional Review Board of Washington University School of Medicine. Participants consented to the use of their data by the scientific community and data sharing terms have been approved by the Washington University Human Research Protection Office.

: Consent for publication has been granted by ADNI administrators.

Document is current

Any future updates will be listed below