A Comparative Study of Machine Learning Techniques for the Detection of Alzheimer's Disease

The timely detection of Alzheimer's disease remains a significant challenge in the medical field, yet machine learning algorithms offer promising solutions. This study delves into the application of machine learning techniques, such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), to discern Alzheimer's disease from MRI brain images. The findings demonstrate KNN's exceptional accuracy of 99.76% in identifying Alzheimer's, outperforming SVM at 98.98%. This research underscores the potential of KNN and SVM in aiding early Alzheimer's detection, potentially leading to more effective treatment strategies and improved patient outcomes.