Machine Learning Techniques for the Early Detection of Alzheimer's Disease: A Systematic Review

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that poses a significant global health challenge. Early and accurate detection is crucial for timely intervention and for the development of new therapies. Machine learning (ML) has emerged as a powerful tool for analyzing the complex, high-dimensional data associated with AD. This systematic review was conducted by searching major academic databases for peer-reviewed literature published between 2020 and 2025. We identified studies that applied ML models to neuroimaging data for the early detection of AD. Information on ML models, datasets, and performance metrics was extracted and synthesized to provide a comprehensive overview of the field. A range of ML models are employed, from traditional supervised learning algorithms like Support Vector Machines (SVM) to more advanced ensemble (e.g., Random Forest) and deep learning methods (e.g., CNNs). Studies consistently show that ensemble and deep learning models achieve high performance (>90% accuracy in many cases), particularly in multiclass classification. However, the field faces persistent challenges, including severe class imbalance in common datasets, issues of data quality and anomalies, and the "black box" nature of complex models, which limits their interpretability and clinical trust. ML models show immense promise for the early and accurate detection of AD. However, for these tools to be successfully translated into clinical practice, future research must focus on developing robust, generalizable, and interpretable models that can effectively address the challenges of data imbalance and pathological heterogeneity.