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

  • Gayakshika GimhaniEmail author
  • Achala Chathuranga Aponso
  • Naomi Krishnarajah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1041)

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.

Keywords

Machine learning Dementia detection Alzheimer’s disease Vascular dementia (VD) Lewy body dementia (DLB) Frontotemporal dementia (FTD) Normal pressure hydrocephalus (NPH) Parkinson’s disease dementia Syphilis and creutzfeldt–jakob disease Artificial neural networks Decision trees Support vector machine MRI CT SPECT FMRI PET EEG CSF 

Notes

Acknowledgements

I would like to express my sincere gratitude to my supervisor Mr. Achala Chathuranga Aponso for providing me continuous support and guidance towards this research. And also, special gratitude for Ahila Arumugam as her research paper on dementia helped me to continue my research work. Special thanks to Informatics Institute of Technology and University of Westminster.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Gayakshika Gimhani
    • 1
    Email author
  • Achala Chathuranga Aponso
    • 1
  • Naomi Krishnarajah
    • 1
  1. 1.Informatics Institute of Technology, University of WestminsterColomboSri Lanka

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