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.
|Title of host publication||Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||11|
|Publication status||Published - 21 Feb 2019|
|Event||11th International Conference on Developments in eSystems Engineering, DeSE 2018 - Cambridge, United Kingdom|
Duration: 2 Sep 2018 → 5 Sep 2018
|Name||Proceedings - International Conference on Developments in eSystems Engineering, DeSE|
|Conference||11th International Conference on Developments in eSystems Engineering, DeSE 2018|
|Period||2/09/18 → 5/09/18|
Bibliographical noteFunding 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.;
© 2018 IEEE.
- Alzheimer's Disease
- Machine Learning