Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease

Mohamed Mahyoub; Martin Randles; Thar Baker; Po Yang

doi:10.1109/HPCC/SmartCity/DSS.2018.00240

Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease

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

This paper investigates data for 9 common Alzheimer's Disease risk factors, from three different categories; Medical History, Lifestyle, and Demography. The dataset used consists of 185 normal control, 177 early mild cognitive impairment, 161 late mild cognitive impairment and 127 Alzheimer's Disease subjects. The initial experiment had training results of 0.92 sensitivity, 0.935 specificity and 0.771 precision. However, during the test stage the final output was 0.741 sensitivity, 0.515 specificity and 0.286 precision. The results of this experiment did not give a clear classification or definite predictive value. Involving more variables and underlying data could provide a better outcome. This paper is a part of a long-term study that focuses on the classification and ranking the importance of Alzheimer's Disease risk factors using Machine Learning predictive models and classifications techniques.

Original language	English
Title of host publication	Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1455-1461
Number of pages	7
ISBN (Electronic)	9781538666142
DOIs	https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00240
Publication status	Published - 22 Jan 2019
Event	20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 - Exeter, United Kingdom Duration: 28 Jun 2018 → 30 Jun 2018

Publication series

Name	Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018

Conference

Conference	20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
Country/Territory	United Kingdom
City	Exeter
Period	28/06/18 → 30/06/18

Bibliographical note

Funding Information:
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.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Publisher Copyright:
© 2018 IEEE.

Keywords

ADNI
Alzheimer's Disease
Classification
Dementia
Machine Learning

Access to Document

10.1109/HPCC/SmartCity/DSS.2018.00240

Cite this

Mahyoub, M., Randles, M., Baker, T., & Yang, P. (2019). Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease. In Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 (pp. 1455-1461). Article 8622978 (Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00240

Mahyoub, Mohamed ; Randles, Martin ; Baker, Thar et al. / Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease. Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1455-1461 (Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018).

@inproceedings{ec9b2bb8a9f4498587bc28cdb37e1c39,

title = "Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease",

abstract = "This paper investigates data for 9 common Alzheimer's Disease risk factors, from three different categories; Medical History, Lifestyle, and Demography. The dataset used consists of 185 normal control, 177 early mild cognitive impairment, 161 late mild cognitive impairment and 127 Alzheimer's Disease subjects. The initial experiment had training results of 0.92 sensitivity, 0.935 specificity and 0.771 precision. However, during the test stage the final output was 0.741 sensitivity, 0.515 specificity and 0.286 precision. The results of this experiment did not give a clear classification or definite predictive value. Involving more variables and underlying data could provide a better outcome. This paper is a part of a long-term study that focuses on the classification and ranking the importance of Alzheimer's Disease risk factors using Machine Learning predictive models and classifications techniques.",

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

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

note = "Funding Information: 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.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer{\textquoteright}s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Publisher Copyright: {\textcopyright} 2018 IEEE.; 20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 ; Conference date: 28-06-2018 Through 30-06-2018",

year = "2019",

month = jan,

day = "22",

doi = "10.1109/HPCC/SmartCity/DSS.2018.00240",

language = "English",

series = "Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018",

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

pages = "1455--1461",

booktitle = "Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018",

}

Mahyoub, M, Randles, M, Baker, T & Yang, P 2019, Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease. in Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018., 8622978, Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1455-1461, 20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018, Exeter, United Kingdom, 28/06/18. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00240

Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease. / Mahyoub, Mohamed; Randles, Martin; Baker, Thar et al.
Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1455-1461 8622978 (Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018).

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

TY - GEN

T1 - Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease

AU - Mahyoub, Mohamed

AU - Randles, Martin

AU - Baker, Thar

AU - Yang, Po

N1 - Funding Information: 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.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Publisher Copyright: © 2018 IEEE.

PY - 2019/1/22

Y1 - 2019/1/22

N2 - This paper investigates data for 9 common Alzheimer's Disease risk factors, from three different categories; Medical History, Lifestyle, and Demography. The dataset used consists of 185 normal control, 177 early mild cognitive impairment, 161 late mild cognitive impairment and 127 Alzheimer's Disease subjects. The initial experiment had training results of 0.92 sensitivity, 0.935 specificity and 0.771 precision. However, during the test stage the final output was 0.741 sensitivity, 0.515 specificity and 0.286 precision. The results of this experiment did not give a clear classification or definite predictive value. Involving more variables and underlying data could provide a better outcome. This paper is a part of a long-term study that focuses on the classification and ranking the importance of Alzheimer's Disease risk factors using Machine Learning predictive models and classifications techniques.

AB - This paper investigates data for 9 common Alzheimer's Disease risk factors, from three different categories; Medical History, Lifestyle, and Demography. The dataset used consists of 185 normal control, 177 early mild cognitive impairment, 161 late mild cognitive impairment and 127 Alzheimer's Disease subjects. The initial experiment had training results of 0.92 sensitivity, 0.935 specificity and 0.771 precision. However, during the test stage the final output was 0.741 sensitivity, 0.515 specificity and 0.286 precision. The results of this experiment did not give a clear classification or definite predictive value. Involving more variables and underlying data could provide a better outcome. This paper is a part of a long-term study that focuses on the classification and ranking the importance of Alzheimer's Disease risk factors using Machine Learning predictive models and classifications techniques.

KW - ADNI

KW - Alzheimer's Disease

KW - Classification

KW - Dementia

KW - Machine Learning

UR - http://www.scopus.com/inward/record.url?scp=85062501609&partnerID=8YFLogxK

U2 - 10.1109/HPCC/SmartCity/DSS.2018.00240

DO - 10.1109/HPCC/SmartCity/DSS.2018.00240

M3 - Conference contribution with ISSN or ISBN

AN - SCOPUS:85062501609

T3 - Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018

SP - 1455

EP - 1461

BT - Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018

Y2 - 28 June 2018 through 30 June 2018

ER -

Mahyoub M, Randles M, Baker T, Yang P. Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease. In Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1455-1461. 8622978. (Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018). doi: 10.1109/HPCC/SmartCity/DSS.2018.00240

Effective Use of Data Science Toward Early Prediction of Alzheimer's Disease

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this