Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)

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Random Forest Algorithm for Alzheimer’s Disease Prediction

Authors
Zakaria Mokadem1, *, Mohamed Djerioui1, Bilal Attalla1, Youcef Brik1
1LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila, 28000, Algeria
*Corresponding author. Email: zakaria.mokadem@univ-msila.dz
Corresponding Author
Zakaria Mokadem
Available Online 5 August 2025.
DOI
10.2991/978-94-6463-805-9_11How to use a DOI?
Keywords
Alzheimer’s disease; Dementia; Random Forest; Neuropsychological Assessment
Abstract

Alzheimer’s disease is a gradient degeneration of essential cognitive activities that mainly affects elderly individuals. Diagnosis of Alzheimer’s disease by neuropsychological assessments is considered an important step in disease management. However, using a single neuropsychological assessment technique often leads to a high rate of misdiagnosis of the disease. To tackle this issue, we built a Random Forest machine learning algorithm with five different neuropsychological assessments to predict Alzheimer’s disease. A total of 1761 samples acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were classified into cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease groups. we create three separate subsets, namely CN vs. AD, CN vs. MCI, and AD vs. MCI for train and test the Random Forest model. Our model achieved high accuracy in distinguishing between the healthy and affected groups, it attained an accuracy of 97.74% with the CN vs. AD subset and 94.65% with the CN vs. MCI subset, making it capable of quickly diagnosis of AD. Our study suggests a robust and efficient Random Forest classifier for Alzheimer’s disease prediction.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
Series
Advances in Intelligent Systems Research
Publication Date
5 August 2025
ISBN
978-94-6463-805-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-805-9_11How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zakaria Mokadem
AU  - Mohamed Djerioui
AU  - Bilal Attalla
AU  - Youcef Brik
PY  - 2025
DA  - 2025/08/05
TI  - Random Forest Algorithm for Alzheimer’s Disease Prediction
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 85
EP  - 93
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_11
DO  - 10.2991/978-94-6463-805-9_11
ID  - Mokadem2025
ER  -