SEAL OPTIMIZATION DRIVEN RANDOM FOREST FRAMEWORK FOR ENHANCED ALZHEIMER'S DISEASE CLASSIFICATION

Alzheimer’s Disease remains a progressive neurodegenerative disorder, necessitating early detection for effective intervention. Traditional diagnostic methods face challenges in accuracy, feature selection, and handling high-dimensional neuroimaging data. The purpose of this study is to enhance classification performance by integrating bio-inspired optimization with Machine Learning. A Seal Optimization-driven Random Forest (SO-RF) framework is introduced to refine feature selection, optimize hyperparameters, and improve decision-making in disease classification. The methodology involves leveraging seal-inspired search strategies to enhance the diversity and robustness of the Random Forest model, ensuring balanced precision and recall. The proposed SO-RF model outperforms conventional approaches in classification accuracy, sensitivity, and specificity, demonstrating its effectiveness in reducing false positives and false negatives. Experimental results validate the model’s superiority in handling complex medical data, confirming its potential for automated Alzheimer’s Disease diagnosis. The optimized classification framework presents a promising solution for advancing computational techniques in neurodegenerative disease detection.