DISEASE PREDICTION USING ARTIFICIAL INTELLIGENCE TECHNIQUES: A COMPREHENSIVE REVIEW OF ALZHEIMERS DISEASE DETECTION

healthcare systems, patients, and caregivers. Early and accurate detection of AD remains challenging due to the complexity of its pathology, the high cost of neuroimaging, and the invasive nature of traditional diagnostic methods. In recent years, artificial intelligence (AI) techniques have emerged as transformative tools for AD prediction, offering improved accuracy, accessibility, and interpretability. This paper provides a comprehensive review of AI-based approaches for Alzheimers disease detection, examining machine learning and deep learning methodologies applied to diverse data modalities including neuroimaging, clinical assessments, behavioral markers, and handwriting analysis. Particular attention is given to recent advances in transfer learning, ensemble methods, explainable AI, and multimodal integration. The review synthesizes findings from cutting-edge research published between 2024 and 2025, highlighting state-of-the-art models achieving accuracy rates exceeding 99% in controlled settings. Key challenges including data imbalance, generalizability, and clinical translation are discussed, along with future directions for AI-driven AD diagnostics within emerging Healthcare 5.0 paradigms.