Feature selection for the classification of Alzheimer's disease data

Hany Alashwal; Areeg Abdalla; Mohamed El Halaby; Ahmed A. Moustafa

doi:10.1145/3378936.3378982

Feature selection for the classification of Alzheimer's disease data

Hany Alashwal, Areeg Abdalla, Mohamed El Halaby, Ahmed A. Moustafa

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

1 Citation (Scopus)

Abstract

![CDATA[In this paper, we describe the features of our large dataset (6400+ rows and 400+ features) that includes Alzheimer's disease (AD) patients, individuals with mild cognitive impairment (MCI, prodromal stage of Alzheimer's disease), and healthy individuals (without AD or MCI). We also, present a feature selection method applied on the dataset. Unlike prior data mining models that were applied to AD, our dataset is big in nature and includes genetic, neural, nutritional, and cognitive measures of all the individuals. All of these measures in the data have been shown by empirical studies to be related to the development of AD. We used a random forest classifier to discover which features best classify and differentiate between AD patients and healthy individuals. Identifying these features will likely provide evidence for protective factors against the development of AD.]]

Original language	English
Title of host publication	Proceedings of the 3rd International Conference on Software Engineering and Information Management (ICSIM 2020), Sydney, Australia, 12-15 January 2020
Publisher	Association for Computing Machinery
Pages	41-45
Number of pages	5
ISBN (Print)	9781450376907
DOIs	https://doi.org/10.1145/3378936.3378982
Publication status	Published - 2020
Event	International Conference on Software Engineering and Information Management - Duration: 12 Jan 2020 → …

Conference

Conference	International Conference on Software Engineering and Information Management
Period	12/01/20 → …

Keywords

Alzheimer's disease
data mining
decision trees
management information systems
mild cognitive impairment

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10.1145/3378936.3378982

Cite this

Alashwal, H., Abdalla, A., El Halaby, M., & Moustafa, A. A. (2020). Feature selection for the classification of Alzheimer's disease data. In Proceedings of the 3rd International Conference on Software Engineering and Information Management (ICSIM 2020), Sydney, Australia, 12-15 January 2020 (pp. 41-45). Association for Computing Machinery. https://doi.org/10.1145/3378936.3378982

@inproceedings{b8a10d8c6cca40a7bfa396157aad6dc4,

title = "Feature selection for the classification of Alzheimer's disease data",

abstract = "![CDATA[In this paper, we describe the features of our large dataset (6400+ rows and 400+ features) that includes Alzheimer's disease (AD) patients, individuals with mild cognitive impairment (MCI, prodromal stage of Alzheimer's disease), and healthy individuals (without AD or MCI). We also, present a feature selection method applied on the dataset. Unlike prior data mining models that were applied to AD, our dataset is big in nature and includes genetic, neural, nutritional, and cognitive measures of all the individuals. All of these measures in the data have been shown by empirical studies to be related to the development of AD. We used a random forest classifier to discover which features best classify and differentiate between AD patients and healthy individuals. Identifying these features will likely provide evidence for protective factors against the development of AD.]]",

keywords = "Alzheimer's disease, data mining, decision trees, management information systems, mild cognitive impairment",

author = "Hany Alashwal and Areeg Abdalla and {El Halaby}, Mohamed and Moustafa, {Ahmed A.}",

year = "2020",

doi = "10.1145/3378936.3378982",

language = "English",

isbn = "9781450376907",

pages = "41--45",

booktitle = "Proceedings of the 3rd International Conference on Software Engineering and Information Management (ICSIM 2020), Sydney, Australia, 12-15 January 2020",

publisher = "Association for Computing Machinery",

note = "International Conference on Software Engineering and Information Management ; Conference date: 12-01-2020",

}

Alashwal, H, Abdalla, A, El Halaby, M & Moustafa, AA 2020, Feature selection for the classification of Alzheimer's disease data. in Proceedings of the 3rd International Conference on Software Engineering and Information Management (ICSIM 2020), Sydney, Australia, 12-15 January 2020. Association for Computing Machinery, pp. 41-45, International Conference on Software Engineering and Information Management, 12/01/20. https://doi.org/10.1145/3378936.3378982

Feature selection for the classification of Alzheimer's disease data. / Alashwal, Hany; Abdalla, Areeg; El Halaby, Mohamed et al.
Proceedings of the 3rd International Conference on Software Engineering and Information Management (ICSIM 2020), Sydney, Australia, 12-15 January 2020. Association for Computing Machinery, 2020. p. 41-45.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Feature selection for the classification of Alzheimer's disease data

AU - Alashwal, Hany

AU - Abdalla, Areeg

AU - El Halaby, Mohamed

AU - Moustafa, Ahmed A.

PY - 2020

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N2 - ![CDATA[In this paper, we describe the features of our large dataset (6400+ rows and 400+ features) that includes Alzheimer's disease (AD) patients, individuals with mild cognitive impairment (MCI, prodromal stage of Alzheimer's disease), and healthy individuals (without AD or MCI). We also, present a feature selection method applied on the dataset. Unlike prior data mining models that were applied to AD, our dataset is big in nature and includes genetic, neural, nutritional, and cognitive measures of all the individuals. All of these measures in the data have been shown by empirical studies to be related to the development of AD. We used a random forest classifier to discover which features best classify and differentiate between AD patients and healthy individuals. Identifying these features will likely provide evidence for protective factors against the development of AD.]]

AB - ![CDATA[In this paper, we describe the features of our large dataset (6400+ rows and 400+ features) that includes Alzheimer's disease (AD) patients, individuals with mild cognitive impairment (MCI, prodromal stage of Alzheimer's disease), and healthy individuals (without AD or MCI). We also, present a feature selection method applied on the dataset. Unlike prior data mining models that were applied to AD, our dataset is big in nature and includes genetic, neural, nutritional, and cognitive measures of all the individuals. All of these measures in the data have been shown by empirical studies to be related to the development of AD. We used a random forest classifier to discover which features best classify and differentiate between AD patients and healthy individuals. Identifying these features will likely provide evidence for protective factors against the development of AD.]]

KW - Alzheimer's disease

KW - data mining

KW - decision trees

KW - management information systems

KW - mild cognitive impairment

UR - https://hdl.handle.net/1959.7/uws:56362

U2 - 10.1145/3378936.3378982

DO - 10.1145/3378936.3378982

M3 - Conference Paper

SN - 9781450376907

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BT - Proceedings of the 3rd International Conference on Software Engineering and Information Management (ICSIM 2020), Sydney, Australia, 12-15 January 2020

PB - Association for Computing Machinery

T2 - International Conference on Software Engineering and Information Management

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ER -

Feature selection for the classification of Alzheimer's disease data

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