Advances in Machine Learning for Alzheimer’s disease Diagnosis: A Comprehensive Overview and Comparative Analysis

This paper comprehensively analyses recent advances in machine learning methods for early Alzheimer’s disease (AD) diagnosis. It underscores the transformative impact of machine learning in healthcare, improving patient-doctor relationships and personalized treatment recommendations. Deep neural networks, graph-based deep learning, and convolutional neural networks (CNNs) have shown promise in accurately identifying AD stages, with classification accuracy ranging from 90% to 97%. Despite improvements, challenges like class imbalance, biomarker selection, and missing feature data persist. Recent advancements like federated learning enable collaborative forecasting across healthcare facilities while ensuring data confidentiality and consistency. This synthesis contributes to understanding the development of machine learning techniques for early AD diagnosis, with implications for clinical practice and future research combating neurodegenerative diseases.