A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data
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 l...
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IMS Vogosca
2024-12-01
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| Series: | Science, Engineering and Technology |
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| Online Access: | https://setjournal.com/SET/article/view/182 |
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| author | Zakaria Mokadem Mohamed Djerioui Bilal Attallah Youcef Brik |
| author_facet | Zakaria Mokadem Mohamed Djerioui Bilal Attallah Youcef Brik |
| author_sort | Zakaria Mokadem |
| collection | DOAJ |
| description | 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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| format | Article |
| id | doaj-art-c555673e4c18466e981c2ec189a3dafb |
| institution | Kabale University |
| issn | 2831-1043 2744-2527 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | IMS Vogosca |
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| series | Science, Engineering and Technology |
| spelling | doaj-art-c555673e4c18466e981c2ec189a3dafb2025-01-05T22:04:13ZengIMS VogoscaScience, Engineering and Technology2831-10432744-25272024-12-015110.54327/set2025/v5.i1.182A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological DataZakaria Mokadem0Mohamed Djerioui1Bilal Attallah2Youcef Brik3LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila 28000, Algeria.LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila 28000, Algeria.LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila 28000, Algeria.LASS Laboratory, Faculty of Technology, University of M’sila, University Pole Road Bordj Bou Arreridj, M’sila 28000, Algeria.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. https://setjournal.com/SET/article/view/182Alzheimer’s diseaseDementiaMachine learningClassificationNeuropsychological assessment |
| spellingShingle | Zakaria Mokadem Mohamed Djerioui Bilal Attallah Youcef Brik A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data Science, Engineering and Technology Alzheimer’s disease Dementia Machine learning Classification Neuropsychological assessment |
| title | A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data |
| title_full | A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data |
| title_fullStr | A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data |
| title_full_unstemmed | A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data |
| title_short | A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data |
| title_sort | comparison of machine learning algorithms for predicting alzheimer s disease using neuropsychological data |
| topic | Alzheimer’s disease Dementia Machine learning Classification Neuropsychological assessment |
| url | https://setjournal.com/SET/article/view/182 |
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