Authors: M. Rohini; D. Surendran
Addresses: Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India ' Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India
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.
Keywords: Alzheimer's disease; MCI; mild cognitive impairment; sMCI; stable mild cognitive impairment; pMCI; progressive mild cognitive impairment; multi-classifier; random forest.
International Journal of Intelligent Engineering Informatics, 2021 Vol.9 No.5, pp.455 - 469
Received: 29 Jan 2021
Accepted: 04 Jul 2021
Published online: 03 Feb 2022 *