Disease Detection for Alzheimer’s using Machine Learning Techniques

This paper presents a comprehensive review of recent advancements in the prediction of Alzheimer’s Disease (AD) using machine learning techniques. We analyze two pivotal studies: one employing deep learning models for Alzheimer’s detection from retinal photographs and another using machine learning techniques alongside Principal Component Analysis (PCA) for AD progression classification. The first study demonstrates the potential of deep learning algorithms in analyzing retinal images, achieving high accuracy, sensitivity, and specificity. The second study explores the efficacy of SVM, RELM, and IVM classifiers in AD prediction, showcasing their performance in both binary and multiclass classification contexts. This review synthesizes the methodologies and results of these studies, highlighting the innovative use of machine learning in medical imaging and data analysis for AD prediction. The findings underscore the potential of machine learning as a diagnostic tool, offering insights into the future of AD detection and progression monitoring.