Diagnosis and Classification of Alzheimer's Disease Using Some Machine Learning Models: A Comparative Study

Section: Article
Issue
Vol. 22 No. 2 (2025): Volume 22 Issue 2
Published
Nov 30, 2025
Pages
26-38

Abstract

Alzheimer's disease damages brain neurons, resulting in memory loss. Early and accurate diagnosis of the disease is crucial for implementing preventive measures. However, differentiating between Alzheimer’s and healthy data in older adults is challenging due to the similarities in their brain patterns and intensities, complicating researchers' efforts to make an accurate diagnosis. Therefore, the research aims to use machine learning to improve diagnosis and classification of the disease, such as support vector machines (SVM), decision trees, and feedforward neural networks (FFNN). Classification algorithms were applied to the Alzheimer’s disease dataset, including 2149 cases, and the models were evaluated through metrics (Accuracy, Precision, Recall, specificity, F1 Score, F2 Score, F3 score, and AUC).


Following data analysis and obtaining the results, we reached the Decision Tree model excels across all metrics, achieving high scores in accuracy (96.32%), precision (94.63%), recall (95%), specificity (97.05%), and AUC (94.96%). This demonstrates its ability to correctly identify true positives and negatives, and reduce false positives and negatives, makes it the most reliable model for accurately classifying Alzheimer's disease cases. In contrast, the SVM linear and FFANN models offer a good balance with accuracy (83.53% and 83.57%), specificity (89.27% and 91.72%), and AUC (89.63% and 89.84%). However, their lower recall (73.03% and 68.68%) compared to the Decision Tree may result in missed positive cases, making them less effective for classification. The SVM RBF model is the least effective option, with high precision and specificity but poor performance across all other metrics and lacks overall balance, resulting in a high number of false negatives. metrics and lacks overall balance, resulting in a high number of false negatives.


Conclusion: The decision tree model outperforms other models, making it the best choice for Alzheimer's disease classification.

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Author

Saman Hussein Mahmood

Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University-Erbil, Iraq.

Identifiers

https://doi.org/10.33899/iqjoss.v22i2.54070
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How to Cite

Mahmood, S. H. . (2025). Diagnosis and Classification of Alzheimer’s Disease Using Some Machine Learning Models: A Comparative Study. IRAQI JOURNAL OF STATISTICAL SCIENCES, 22(2), 26–38. https://doi.org/10.33899/iqjoss.v22i2.54070