Analysis of State of Art Machine Learning Models for Classification Prediction of Alzheimer’s Disease
Keywords:
Alzheimer’s disease (AD), Dementia, Machine Learning, Classification, Prediction, PerformanceAbstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that leads to dementia and cognitive impairment, significantly contributing to age-related cognitive decline and widespread memory loss. As the population ages, AD will increasingly impact individuals, their families, and healthcare systems, resulting in substantial social, financial, and economic challenges for aging societies. Early detection of AD is crucial, as it allows for more effective intervention compared to treatment at later stages of the disease. Machine Learning (ML) techniques, applied to Magnetic Resonance Imaging (MRI) data, offer a promising approach for the early detection of AD. This study evaluates twenty different ML models to classify dementia patients as either AD or non-AD. The performance of these ML models is assessed using metrics such as Precision, Recall, Accuracy, and F1-score.
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