Multiclassifier learning for the early prediction of dementia disease progression from MCI

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.