A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data

Authors

  • Zakaria Mokadem LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila 28000, Algeria.
  • Mohamed Djerioui LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila 28000, Algeria.
  • Bilal Attallah LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila 28000, Algeria.
  • Youcef Brik LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila 28000, Algeria.

DOI:

https://doi.org/10.54327/set2025/v5.i1.182

Keywords:

Alzheimer’s disease, Dementia, Machine learning, Classification, Neuropsychological assessment

Abstract

Alzheimer’s disease (AD) is a gradient degeneration of essential cognitive activities such as memory, thinking, and cognition. AD mainly affects elderly individuals and is recognized as the most common cause of dementia. This study investigates the predictive performance of nine supervised machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, eXtreme Gradient Boost, and Gradient Boosting—using neuropsychological assessment data. We applied two classification techniques—binary and multiclass—to classify 1761 subjects into three categories: cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Binary classification tasks focused on CNvsAD and CNvsMCI subsets, while multiclass classification used the full dataset (TriClass). Hyperparameter tuning was performed to optimize model performance. The results indicate that ensemble learning models, particularly Gradient Boosting (GB) and Random Forest (RF), exhibited superior accuracy compared to other algorithms. Most models for the CNvsAD subset achieved the highest accuracy (97.74%), while GB achieved the best performance (94.98%) for the CNvsMCI subset. For multiclass classification, RF achieved the highest accuracy at 84.70%. These findings highlight the robustness and efficiency of ensemble learning algorithms, especially in handling complex, non-linear data structures. This study underscores the potential of RF and GB as reliable tools for early detection and classification of Alzheimer’s disease using neuropsychological data.

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Author Biography

  • Zakaria Mokadem, LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila 28000, Algeria.

    Laboratory of analysis of signals and systems (LASS), Department of Electronics, Faculty of Technology, University of M'sila, M’sila, Algeria.

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Published

23.12.2024

Data Availability Statement

The data used in this study was obtained from the Alzheimer’s Disease Neuro-imaging Initiative (ADNI) database (http://adni.loni.usc.edu) and is available with permission to all researchers.

How to Cite

[1]
Z. Mokadem, M. Djerioui, B. Attallah, and Y. Brik, “A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data”, Sci. Eng. Technol., vol. 5, no. 1, Dec. 2024, doi: 10.54327/set2025/v5.i1.182.

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