Close menu


Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

ADPM: An Alzheimer’s Disease Prediction Model for Time Series Neuroimage Analysis

Hong, Hin, Lin, Rongjie, Yang, Chenhui, Cai, Chunting and Clawson, Kathy (2020) ADPM: An Alzheimer’s Disease Prediction Model for Time Series Neuroimage Analysis. IEEE Access, 8 (1). pp. 62601-62609. ISSN 2169-3536

Item Type: Article


Alzheimer’s Disease (AD) is a form of dementia which causes memory, thinking, and behavior disorders in humans. Effective early diagnosis and treatment of AD is of fundamental importance as it can reduce disease progression, allow more effective management of symptoms, facilitate timely patient access to advice and support, and lower associated costs of health care. Given that Alzheimer’s typically progresses in stages over an extended period of time, we propose that automated analysis of time sequential data may enhance disease prediction. We present a novel time-series Alzheimer’s Disease Prediction Model (ADPM) comprising Random Forest (RF) region of
interest (ROI) selection and Gated Recurrent Units (GRU) prediction. Experiments show that our methodology achieves higher classification accuracy in comparison to existing algorithms, and can facilitate prediction of early onset AD. Furthermore, testing demonstrates that random forest ROI selection can identify disease-relative brain regions across different image modalities (MRI, PET, DTI).

[img] PDF
stamp.jsp_arnumber=9032212&tag=1 - Published Version
Available under License Creative Commons Attribution.

Download (2kB)

More Information

Depositing User: Kathy Clawson


Item ID: 11926
Identification Number:
ISSN: 2169-3536
Official URL:

Users with ORCIDS

ORCID for Kathy Clawson: ORCID iD

Catalogue record

Date Deposited: 15 Apr 2020 17:13
Last Modified: 30 Sep 2020 11:03


Author: Kathy Clawson ORCID iD
Author: Hin Hong
Author: Rongjie Lin
Author: Chenhui Yang
Author: Chunting Cai

University Divisions

Faculty of Technology > School of Computer Science

Actions (login required)

View Item (Repository Staff Only) View Item (Repository Staff Only)