Critical Evaluation of Different Biomarkers and Machine-Learning-Based Approaches to Identify Dementia Disease in Early Stages

Abstract

Dementia is the loss of cognitive functioning and behavioural abilities to some extent. This is a major neurocognitive disorder which is a group of symptoms caused by other different conditions. Alzheimer’s disease has considered as the most common type of dementia. Apart from that, vascular dementia (VD), Lewy body dementia (DLB), frontotemporal dementia (FTD), Parkinson’s disease dementia, normal pressure hydrocephalus (NPH), Creutzfeldt–Jakob disease and syphilis are under Dementia. The cure for this disease is yet to be found, hence recognizing the disease in early stages and delaying the progress is a highly important fact. So, the investigation of this disease will remain as an open challenge. The aim of this paper is to review biomarkers and selected machine-learning techniques that can be segregated into early detection of dementia. Various machine-learning techniques such as artificial neural networks, decision trees and support vector machine will be discussed in this paper to find a better approach to identify Dementia in early stages. Especially this paper is consisting of following sections: (i) A brief description of Dementia and each type and the global statistics; (ii) A review of various type of medical techniques to identify dementia (MRI, CT, SPECT, fMRI, PET, EEG and CSF); (iii) Pre-processing signals; (iv) A review of machine-learning techniques.