research-article

Feature Selection for the Classification of Alzheimer's Disease Data

Authors:

Mohamed El Halaby,

Ahmed A. MoustafaAuthors Info & Claims

ICSIM '20: Proceedings of the 3rd International Conference on Software Engineering and Information Management

Pages 41 - 45

https://doi.org/10.1145/3378936.3378982

Published: 07 March 2020 Publication History

Abstract

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.

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Wu XOniani DShao ZArciero PSivarajkumar SHilsman JMohr AIbe SMoharir MLi LJain RChen JWang Y(2025)A Scoping Review of Artificial Intelligence for Precision NutritionAdvances in Nutrition10.1016/j.advnut.2025.100398(100398)Online publication date: Feb-2025
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Alshehhi TAyesh AYang YChen F(2024)Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models1Technology and Health Care10.3233/THC-220598(1-18)Online publication date: 4-Jan-2024
https://doi.org/10.3233/THC-220598
Manimekalai K. Abirami D. (2022)Principles and Methods of Explainable Artificial Intelligence in HealthcarePrinciples and Methods of Explainable Artificial Intelligence in Healthcare10.4018/978-1-6684-3791-9.ch012(272-292)Online publication date: 20-May-2022
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Index Terms

Feature Selection for the Classification of Alzheimer's Disease Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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ICSIM '20: Proceedings of the 3rd International Conference on Software Engineering and Information Management

January 2020

258 pages

ISBN:9781450376907

DOI:10.1145/3378936

Copyright © 2020 ACM.

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University of Science and Technology of China: University of Science and Technology of China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2020

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United Arab Emirates University

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ICSIM '20

ICSIM '20: The 3rd International Conference on Software Engineering and Information Management

January 12 - 15, 2020

NSW, Sydney, Australia

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Cited By

Wu XOniani DShao ZArciero PSivarajkumar SHilsman JMohr AIbe SMoharir MLi LJain RChen JWang Y(2025)A Scoping Review of Artificial Intelligence for Precision NutritionAdvances in Nutrition10.1016/j.advnut.2025.100398(100398)Online publication date: Feb-2025
https://doi.org/10.1016/j.advnut.2025.100398
Alshehhi TAyesh AYang YChen F(2024)Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models1Technology and Health Care10.3233/THC-220598(1-18)Online publication date: 4-Jan-2024
https://doi.org/10.3233/THC-220598
Manimekalai K. Abirami D. (2022)Principles and Methods of Explainable Artificial Intelligence in HealthcarePrinciples and Methods of Explainable Artificial Intelligence in Healthcare10.4018/978-1-6684-3791-9.ch012(272-292)Online publication date: 20-May-2022
https://doi.org/10.4018/978-1-6684-3791-9.ch012

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