Comprehensive Review of Machine Learning Approaches for Alzheimer’s Disease Diagnosis and Prognosis

Abstract

Alzheimer’s disease (AD) represents one of the most pressing challenges in modern healthcare, owing to its progressive nature, lack of curative treatments, and increasing global prevalence. In recent years, machine learning (ML) has emerged as a powerful tool to aid in the early diagnosis and prognosis of AD, offering data-driven approaches capable of managing high-dimensional, heterogeneous, and multimodal data. This review provides a comprehensive synthesis of ML techniques applied to AD, including supervised, unsupervised, and reinforcement learning algorithms. Particular emphasis is placed on models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Recurrent Neural Networks (RNNs), which demonstrate strong performance in classifying disease stages and predicting cognitive decline.
The review systematically analyzes studies published between 2014 and 2024, outlining prevailing approaches in feature selection, data preprocessing, and model evaluation. Major datasets—including ADNI, NACC, and OASIS—are discussed in terms of accessibility, modality, and clinical relevance. The paper also highlights challenges related to data imbalance, interpretability, and generalizability across clinical settings. Despite promising advances, the integration of explainable AI (XAI) frameworks remains limited. Future work must prioritize the development of balanced models that combine predictive accuracy with clinical interpretability to foster real-world deployment and personalized healthcare in AD management.