Comparison analysis of machine learning algorithms to rank Alzheimer's disease risk factors by importance

Mohamed Mahyoub, Martin Randles, Thar Baker, Po Yang

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review

Abstract

People have always feared aging, and the increasing rate of dementia disease caused this fear to twofold. Dementia is irreversible, unstoppable and has no known cure. According to Alzheimer's Disease International 2015 and World Alzheimer Report 2015, the estimated financial cost for healthcare services of Alzheimer's Disease is $1 Trillion in 2018. This paper discusses the importance of investigating Alzheimer's Disease using machine learning, the need to use both behavioural and biological markers data, and a computational method to rank Alzheimer's Disease risk factors by importance using different machine learning models on Alzheimer's Disease clinical assessment data from ADNI. The dataset contains Alzheimer's Disease risk factors data related to medical history, family dementia history, demographical, and some lifestyle data for 1635 subjects. There are 387 normal control, 87 significant memory concerns, 289 early mild cognitive impairment, 539 late mild cognitive impairment and 333 Alzheimer's Disease subjects. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The results show that some risk factors in subjects genetically, demography and lifestyle are more important than some medical history risk factors. Having APOE4, education level, age, weight, family dementia history, and type of work rank as more influential among Alzheimer's Disease subjects.

Original languageEnglish
Title of host publicationProceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-11
Number of pages11
ISBN (Electronic)9781538667125
DOIs
Publication statusPublished - 21 Feb 2019
Event11th International Conference on Developments in eSystems Engineering, DeSE 2018 - Cambridge, United Kingdom
Duration: 2 Sep 20185 Sep 2018

Publication series

NameProceedings - International Conference on Developments in eSystems Engineering, DeSE
Volume2018-September
ISSN (Print)2161-1343

Conference

Conference11th International Conference on Developments in eSystems Engineering, DeSE 2018
Country/TerritoryUnited Kingdom
CityCambridge
Period2/09/185/09/18

Bibliographical note

Funding Information:
The data used in this research was provided by Alzheimer's Disease Neuroimaging Initiative (ADNI). We thank our Professor Danielle J Harvey from University of California, Davis who provided insight and expertise that greatly assisted us in understanding and using the datasets. *Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report1. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.;

Publisher Copyright:
© 2018 IEEE.

Keywords

  • ADNI
  • Alzheimer's Disease
  • Classification
  • Dementia
  • Lifestyle
  • Machine Learning

Cite this