Leveraging Big Data Analytics and Artificial Intelligence for Early Detection and Diagnosis of Alzheimer's Disease

Abstract

The early detection of Alzheimer’s disease (AD) is crucial for effective treatment and management, yet traditional diagnostic methods often fail to identify the disease in its initial stages. Recent advancements in artificial intelligence (AI) and big data analytics have introduced innovative approaches to Alzheimer’s diagnostics, leveraging models like convolutional neural networks (CNNs), Random Forests, and XGBoost. This study compares the performance of these AI-based models in distinguishing between Alzheimer’s patients and healthy individuals, focusing on accuracy, precision, recall, F1 score, and ROC-AUC metrics. Results indicate that CNNs, particularly when combined with ensemble methods like XGBoost, achieve the highest accuracy and balanced diagnostic performance, reaching over 94% accuracy with enhanced sensitivity and specificity. Ensemble models excel in integrating imaging and structured data, minimizing misclassification errors and providing comprehensive diagnostic insights. Although challenges such as data requirements, computational complexity, and interpretability persist, solutions like explainable AI and multi-modal data integration show potential to improve these models' clinical applicability. These findings underscore the transformative impact of AI in Alzheimer’s diagnostics, offering pathways for more timely and accurate detection of neurodegenerative diseases.