<![CDATA[The rapid identification and diagnosis of Alzheimer's disease (AD) is essential for initiating early intervention and effective treatment planning. Magnetic resonance imaging (MRI) provides valuable structural insights into the pathological alterations in the brain associated with AD. Early and accurate detection of AD is critical for initiating timely interventions. This study presents a classical machine learning (ML) approach for detecting AD using structured features extracted from MRI metadata, such as mini-mental state examination (MMSE) scores, brain volume metrics, and cognitive attributes. Unlike deep learning models that rely on raw imaging data, the interpretable framework offers reduced computational complexity and better alignment with real-world clinical constraints. Models such as random forest (RF) and extreme gradient boosting (XGBoost) achieved up to 85% accuracy, showing strong potential for deployment in resource-limited environments. The results demonstrate the potential of classical ML in supporting early AD diagnosis, particularly in low-resource clinical settings. Moreover, the proposed approach offers a computationally efficient and interpretable alternative to deep learning models, facilitating adoption in real-world healthcare environments.]]>
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