A Hybrid Machine Learning Framework for Early Diagnosis of Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) is neurodegenerative disease, making its initial detection essential for suitable treatment planning and for slowing cognitive decline. This research addresses the challenge of accurately identifying Alzheimer’s in its initial stages, where subtle structural and functional brain changes often remain undetected through conventional diagnostic methods. Despite advancements in diagnostic technologies, a significant gap still exists in achieving both high accuracy and reliability for early detection. To bridge this gap, we propose a hybrid machine learning approach that integrates CNNs used feature extraction with a Support Vector Machine classifier of final decision-making. CNN effectively extracts discriminative spatial features from MRI and fMRI scans, while the SVM enhances classification by refining decision boundaries. The performance of Models was evaluated using metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the proposed framework achieves 94.2% accuracy, 96% precision, 95% recall, and a 95.5% F1-score, outperforming conventional standalone techniques. These conclusions highlight the robustness the hybrid model for capturing complex patterns for reliable early diagnosis of AD. Furthermore, this study presents an efficient diagnostic tool that can support clinicians in timely interventions. Future research may extend this work by integrating multimodal data to further enhance predictive performance.