A Review on Alzheimer Disease Classification using different ML and DL Models

Abstract

In this comprehensive review, various machine learning (ML) and deep learning (DL) models are evaluated for their effectiveness in classifying Alzheimer's disease. The study examines a range of methodologies and techniques employed in the classification process, encompassing diverse ML algorithms such as Support Vector Machines (SVM), Random Forests, and k-Nearest Neighbors (k-NN), as well as DL architectures like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Evaluating these models' performance metrics, including accuracy, sensitivity, and specificity, sheds light on their comparative strengths and weaknesses in accurately diagnosing Alzheimer's disease. By synthesizing findings from multiple studies, this review provides valuable insights into the state-of-the-art approaches and identifies promising directions for future research aimed at enhancing Alzheimer's disease classification accuracy and clinical applicability.