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

Mohamed Mahyoub; Martin Randles; Thar Baker; Po Yang

doi:10.1109/DeSE.2018.00008

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

Mohamed Mahyoub, Martin Randles, Thar Baker, Po Yang

School of Arch, Tech and Eng

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-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 language	English
Title of host publication	Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-11
Number of pages	11
ISBN (Electronic)	9781538667125
DOIs	https://doi.org/10.1109/DeSE.2018.00008
Publication status	Published - 21 Feb 2019
Event	11th International Conference on Developments in eSystems Engineering, DeSE 2018 - Cambridge, United Kingdom Duration: 2 Sept 2018 → 5 Sept 2018

Publication series

Name	Proceedings - International Conference on Developments in eSystems Engineering, DeSE
Volume	2018-September
ISSN (Print)	2161-1343

Conference

Conference	11th International Conference on Developments in eSystems Engineering, DeSE 2018
Country/Territory	United Kingdom
City	Cambridge
Period	2/09/18 → 5/09/18

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

Access to Document

10.1109/DeSE.2018.00008

Cite this

Mahyoub, M., Randles, M., Baker, T., & Yang, P. (2019). Comparison analysis of machine learning algorithms to rank Alzheimer's disease risk factors by importance. In Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018 (pp. 1-11). Article 8648588 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE; Vol. 2018-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DeSE.2018.00008

Mahyoub, Mohamed ; Randles, Martin ; Baker, Thar et al. / Comparison analysis of machine learning algorithms to rank Alzheimer's disease risk factors by importance. Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1-11 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE).

@inproceedings{635cacde90b1441ab5c0dc729fa4138a,

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

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.",

keywords = "ADNI, Alzheimer's Disease, Classification, Dementia, Lifestyle, Machine Learning",

author = "Mohamed Mahyoub and Martin Randles and Thar Baker and Po Yang",

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{\textquoteright}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{\textquoteright}s Association; Alzheimer{\textquoteright}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: {\textcopyright} 2018 IEEE.; 11th International Conference on Developments in eSystems Engineering, DeSE 2018 ; Conference date: 02-09-2018 Through 05-09-2018",

year = "2019",

month = feb,

day = "21",

doi = "10.1109/DeSE.2018.00008",

language = "English",

series = "Proceedings - International Conference on Developments in eSystems Engineering, DeSE",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--11",

booktitle = "Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018",

}

Mahyoub, M, Randles, M, Baker, T & Yang, P 2019, Comparison analysis of machine learning algorithms to rank Alzheimer's disease risk factors by importance. in Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018., 8648588, Proceedings - International Conference on Developments in eSystems Engineering, DeSE, vol. 2018-September, Institute of Electrical and Electronics Engineers Inc., pp. 1-11, 11th International Conference on Developments in eSystems Engineering, DeSE 2018, Cambridge, United Kingdom, 2/09/18. https://doi.org/10.1109/DeSE.2018.00008

Comparison analysis of machine learning algorithms to rank Alzheimer's disease risk factors by importance. / Mahyoub, Mohamed; Randles, Martin; Baker, Thar et al.
Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1-11 8648588 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE; Vol. 2018-September).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

TY - GEN

T1 - Comparison analysis of machine learning algorithms to rank Alzheimer's disease risk factors by importance

AU - Mahyoub, Mohamed

AU - Randles, Martin

AU - Baker, Thar

AU - Yang, Po

N1 - 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.

PY - 2019/2/21

Y1 - 2019/2/21

N2 - 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.

AB - 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.

KW - ADNI

KW - Alzheimer's Disease

KW - Classification

KW - Dementia

KW - Lifestyle

KW - Machine Learning

UR - http://www.scopus.com/inward/record.url?scp=85063138284&partnerID=8YFLogxK

U2 - 10.1109/DeSE.2018.00008

DO - 10.1109/DeSE.2018.00008

M3 - Conference contribution with ISSN or ISBN

AN - SCOPUS:85063138284

T3 - Proceedings - International Conference on Developments in eSystems Engineering, DeSE

SP - 1

EP - 11

BT - Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th International Conference on Developments in eSystems Engineering, DeSE 2018

Y2 - 2 September 2018 through 5 September 2018

ER -

Mahyoub M, Randles M, Baker T, Yang P. Comparison analysis of machine learning algorithms to rank Alzheimer's disease risk factors by importance. In Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1-11. 8648588. (Proceedings - International Conference on Developments in eSystems Engineering, DeSE). doi: 10.1109/DeSE.2018.00008

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this