Multiclassifier learning for the early prediction of dementia disease progression from MCI
by M. Rohini; D. Surendran
International Journal of Intelligent Engineering Informatics (IJIEI), Vol. 9, No. 5, 2021

Abstract: Recently many machine learning and deep learning prediction models have been proposed for the early detection and classification of Alzheimer's disease (AD). AD pathology causes mild cognitive impairment (MCI). The proposed study intends to develop a machine learning model that utilises a relevant subset of predictors to diagnose the progression of the disease. The conversion from MCI to stable MCI (sMCI) or progressive MCI (pMCI) is identified at early stage of onset of symptoms. The quality of existing research works lies in more early identification of disease that greatly affects subjects' recovery. This study utilised mini-mental state exam (MMSE), clinical dementia rating (CDR), estimated total intracranial volume, normalise whole brain volume, and Atlas scaling factor for constructing randomised trees and thus predicting the progression of disease stages from MCI to Alzheimer's disease that causes Dementia. The proposed model proved to give robust classification results that are sufficient for future clinical implementation.

Online publication date: Thu, 03-Feb-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Engineering Informatics (IJIEI):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email