Alzheimer Detection And Classification Using SVM

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's disease (AD) is a progressive brain disorder that leads to memory loss and a gradual decline in thinking and reasoning abilities. One of the major challenges in dealing with Alzheimer's is detecting it early and accurately using MRI brain scans. Traditional manual analysis of these scans can be slow, complex, and prone to human mistakes over the years, different machine learning (ML) models like Decision Tree, Random Forest, Logistic Regression, and K-Nearest Neighbors have been used to identify Alzheimer's. However, these models often face issues such as overfitting, lower accuracy, and weak performance when dealing with complex and high-dimensional MRI data.to overcome these limitations, the proposed approach uses SVM model for detecting and classifying Alzheimer's disease. The SVM model is well-suited for handling non-linear and complex data. It can effectively separate different disease categories by using advanced kernel functions and optimal hyperplane techniques. This leads to more precise and stable classification results, even with smaller datasets compared to existing ML models, the proposed SVM model achieves higher accuracy, sensitivity, and specificity, making it more dependable for automatic Alzheimer's detection. It not only reduces errors but also helps in identifying the disease at an early stage, which is crucial for better treatment and patient care. With 98.16% classification accuracy, it outperforms current architectures significantly in the Alzheimer Detection and Classification Using SVM.

Key findings

• This leads to more precise and stable classification results, even with smaller datasets compared to existing ML models, the proposed SVM model achieves higher accuracy, sensitivity, and specificity, making it more dependable for automatic Alzheimer's detection.
• With 98.16% classification accuracy, it outperforms current architectures significantly in the Alzheimer Detection and Classification Using SVM.

Why it matters

This study used a support vector machine model to detect and classify Alzheimer's disease from MRI brain scans, reporting 98.16% classification accuracy and better accuracy, sensitivity and specificity than other machine learning models. It is a diagnostic classification tool only and does not test any treatment, cure or prevention.

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Study details

• Effect size98.16%
• Evidence strengthLow

Read the original

• DOI: 10.5281/zenodo.20065683
• Original source →
• Free full text: Zenodo (CERN European Organization for Nuclear Research) →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{manogna2026-3541ccba,
  author  = {Manogna, V and Durga, B and Sravani, P and Yamuna, N and Reshma, B},
  title   = {{Alzheimer Detection And Classification Using SVM}},
  journal = {Zenodo (CERN European Organization for Nuclear Research)},
  year    = {2026},
  doi     = {10.5281/zenodo.20065683},
  url     = {https://doi.org/10.5281/zenodo.20065683}
}

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