Application of Machine Learning Algorithms in Early Detection of Alzheimer's Disease

This paper investigates the application of machine learning algorithms for the early detection of Alzheimer's disease. The study evaluates multiple classification algorithms including support vector machines, random forests, and deep neural networks using neuroimaging and clinical datasets. Feature selection and dimensionality reduction techniques are applied to improve model performance and interpretability. The results demonstrate that machine learning approaches can achieve high diagnostic accuracy in identifying early-stage Alzheimer's disease, offering a promising avenue for non-invasive and cost-effective screening tools in clinical practice.

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