Alzheimer’s disease (AD), a progressive neurodegenerative disease, is a major global public health problem. Early and accurate diagnosis is crucial for timely intervention, especially with the prevalence of the condition expected to triple by 2050. Traditional methods have been enhanced by artificial intelligence (AI)–driven techniques, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). These approaches enhance the early detection and classification of diseases by evaluating complex neuroimaging data. Using Matrix Laboratory, we developed hybrid models integrating CNNs and SVMs to detect AD, focusing on feature extraction, predictive accuracy, and model interpretability for clinical use. While Internet of Things-–based wearable devices are reviewed for their potential in large-scale data processing and real-time monitoring, our empirical work emphasizes practical AI solutions. In conclusion, integration of multimodal neuroimaging data and advanced feature selection techniques holds the potential to enhance diagnostic precision of AD.
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