Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly affects memory, cognition, and daily functioning, making early detection crucial for timely intervention and effective treatment planning. Traditional diagnostic methods, relying on clinical assessments and neuropsychological tests, often fail to identify the disease in its initial stages. Recent advances in machine learning (ML) offer promising approaches for analyzing complex neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), to detect early structural and functional brain changes associated with AD. This study proposes a machine learning based framework for early detection of Alzheimer's disease using neuroimaging biomarkers. Various supervised learning algorithms, including support vector machines, random forests, and convolutional neural networks, are applied to extract discriminative patterns from imaging data. The models are evaluated using performance metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve. Experimental results demonstrate that ML-based models outperform conventional diagnostic approaches in classifying earlystage Alzheimer's, providing a potential pathway for clinical decision support systems. This research highlights the role of neuroimaging-driven ML approaches in enhancing early diagnosis, thereby contributing to improved patient care and advancing precision medicine in neurodegenerative disorders.
Alzheimer's Disease, Early Detection, Machine Learning, Neuroimaging, MRI, PET, Biomarkers, Convolutional Neural Networks, Classification, Precision Medicine