A Systematic Review of Techniques for Early-Stage Alzheimer's Disease Diagnosis Using Machine Learning and Deep Learning
Open accessAbstract
Alzheimer's disease (AD) is a brain-related disease in which the patient's condition worsens over time. Early diagnosis of AD is uniquely challenging due to its primarily neurological and cognitive anomalies, such as beta-amyloid plaque accumulation, tau tangles, and hippocampal atrophy, which are subtle and slowly progressive. Unlike diseases with clear physical or biochemical markers, AD anomalies often overlap with normal aging and require analyzing complex data such as brain imaging and cognitive tests. This study reviews various machine learning and deep learning techniques for early AD detection. The findings reveal that deep learning techniques, especially convolutional neural networks (CNNs), and hybrid architectures such as VGG16 and CNN–long short-term memory models achieve near-perfect performance (accuracy of 99.95% and 99.92%, respectively), while traditional models such as support vector machines excel in smaller datasets (accuracy between 90% and 96%) due to their simplicity and interpretability. The combination of multimodal data (magnetic resonance imaging, positron emission tomography, and cerebrospinal fluid biomarkers) significantly improved diagnostic performance, indicating the potential of innovative artificial intelligence (AI) methods in early AD identification. However, deep learning faces key limitations, including high computational demands, lack of interpretability, and reliance on large, annotated datasets, which can hinder clinical applicability. By pursuing learning techniques, such as transfer learning, to reduce data needs and explainable AI to achieve interpretability, researchers and developers can overcome the bottlenecks and enhance the accuracy and efficiency of real-world AD diagnosis. Received: 17 December 2024 | Revised: 9 April 2025 | Accepted: 1 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Md Minul Alam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review and editing, Visualization. Shahram Latifi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
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Few reviewed studies measured the economic impact or long-term feasibility of behavioral interventions within public health systems.
“few studies measured economic impact or long-term feasibility within public health systems [3,44].”
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The Methodology and Discussion and Conclusion sections review machine-learning approaches to Alzheimer’s diagnosis and discuss computational costs and clinical applicability. They do not say whether reviewed studies measured the economic impact or long-term feasibility of behavioral interventions in public health systems (searched for those terms and cost-effectiveness). (LLM, conf=0.76)
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- Authors
- Alam, M. M., & Latifi, S.
- Affiliations
- University of Nevada, Las Vegas (US)
- Venue
- Journal of Data Science and Intelligent Systems
- Publisher
- Bon View Publishing Pte
- Published
- 2025-09-05
- ISSN
- 2972-3841 (ISSN-L)
- OpenAlex
- W4414025830
- Cited by
- 6
- References
- 49
- Citations by year
- 2026: 1 · 2025: 5
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Data Science and Intelligent Systems (publishedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, early diagnosis, machine learning, deep learning, convolutional neural networks, multimodal fusion, neuroimaging, cerebrospinal fluid biomarkers, positron emission tomography, magnetic resonance imaging, transfer learning, explainable AI, support vector machine, CNN-LSTM hybrid model
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