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A Comparison of Machine Learning and Deep Learning Techniques for Predicting Alzheimer's Disease
Abstract
Alzheimer's disease (AD) is a degenerative condition that can cause anything from a slight loss of memory to total loss of consciousness and speech. Early detection has a critical role in maintaining the patient's quality of life. Despite a wealth of studies on AD diagnosis, early and correct diagnosis is most beneficial to patients. Because machine learning (ML) models may identify abnormalities early on, they have become indispensable in the diagnosis of diseases such as AD. ML and computer-aided diagnostics (CAD) have been combined, and this has enhanced AD detection—especially when integrating with MRI data. ML methods are preferred because they produce results quickly and accurately. The goal of this research is to create an automated AD detection system that is more sophisticated and accurate by integrating data from many modalities. The goal of this strategy is to lower the rate of incorrect diagnoses while offering a more thorough diagnostic, emphasizing accuracy, sensitivity, and specificity.
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