Comparative Evaluation of Feature Selection Techniques and Machine Learning Algorithms for Alzheimer's Disease Staging
DOI:
https://doi.org/10.22399/ijcesen.1077Keywords:
Alzheimer's disease, Feature selection, Machine learning, Feature extraction, Texture analysisAbstract
Dementia encompasses a range of brain disorders characterized by cognitive decline, with memory loss as a hallmark symptom. Alzheimer's disease (AD), the most common form of dementia, progressively affects cognitive functions, leading to severe memory loss. Early and accurate detection of AD is essential for timely intervention, preventing further neuronal damage, and improving patient outcomes. This study employs machine learning (ML) techniques, feature selection methods, and texture analysis to enhance AD diagnosis. By systematically evaluating various feature selection techniques and Principal Component Analysis (PCA) in conjunction with multiple ML algorithms, the study identifies the most effective approach for classifying AD stages. The integration of texture-based features with ML models demonstrates a significant improvement in distinguishing Cognitive Normal, Mild Cognitive Impairment, and AD stages. These findings highlight the clinical significance of combining feature selection and texture analysis with ML for early AD diagnosis, facilitating more precise disease classification and contributing to personalized treatment strategies.
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