GENETIC BIOMARKERS DETECTION FOR ALZHEIMER’S DISEASE** | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Volume 25, Issue 1, March 2025, Page 51-73 PDF (737.85 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2025.375039.1388 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Reham Ashraf Shafik   1; Mahmoud Monir 2; Yasmine M. Afify 3; Nagwa Badr 4
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| 1Nasr city | ||||
| 2Faculty of Computer and Information Sciences, Ain Shams University | ||||
| 3Information Systems, Faculty of Computer and Information Sciences, Ain Shams University | ||||
| 4Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt | ||||
| Abstract | ||||
| Alzheimer’s disease remains a complex condition with an unclear cause and no known cure. Current treatments focus on symptom management and slowing disease progression. Research is ongoing to uncover its underlying mechanisms, develop effective treatments, and explore early detection and prevention strategies. Genetic data plays a crucial role in Alzheimer’s detection, offering significant advantages. Genome-wide association studies (GWAS) have identified numerous genetic variants linked to the disease. Large-scale genetic analyses help researchers understand disease pathways, identify potential drug targets, and contribute to novel therapeutic developments. This review aims to highlight research gaps and limitations while proposing future directions for advancing the field. It provides a detailed survey outlining essential criteria for improving genetic-based detection methods. Researchers can enhance accuracy by selecting optimal approaches for genetic analysis. The review focuses on recent studies that integrate genetic data with artificial intelligence (AI) to identify mutated genes associated with Alzheimer’s and classify the disease efficiently. Findings indicate that, despite a relatively small body of published research, studies in this field have grown exponentially since 2020. This review offers a comprehensive analysis of genetic and AI-driven approaches for Alzheimer’s detection. It serves as a valuable resource for researchers, clinicians, and policymakers, shedding light on the current state of the field, guiding future research, and supporting the development of more accurate and effective early detection methods for Alzheimer’s disease. | ||||
| Keywords | ||||
| Alzheimer's detection; Artificial Intelligence; Genetic datasets; Gene Biomarker | ||||
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