REVOLUTIONIZING NEURODEGENERATIVE CARE: IOMT AND DEEP LEARNING SYNERGY FOR ALZHEIMER'S DISEASE PREDICTION IN HEALTHCARE 5.0

Abstract

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide, significantly affecting cognitive function, memory, and daily living activities among elderly populations. Early diagnosis remains a critical challenge due to the gradual progression of symptoms and the complexity of neurological assessments. Healthcare 5.0 integrates advanced digital technologies such as the Internet of Medical Things (IoMT), Artificial Intelligence (AI), deep learning, cloud computing, and intelligent decision-support systems to enable personalized, predictive, and patient-centric healthcare services. This research presents an IoMT-enabled deep learning framework for the early prediction of Alzheimer's disease by integrating wearable medical devices, biomedical imaging, electronic health records, and real-time physiological monitoring. The proposed framework utilizes Convolutional Neural Networks (CNNs) for MRI image analysis, Long Short-Term Memory (LSTM) networks for temporal health monitoring, and cloud-edge computing for efficient data processing and secure storage. IoMT devices continuously collect patient health parameters, enabling proactive disease monitoring and timely clinical intervention. The proposed Healthcare 5.0 architecture enhances diagnostic accuracy, minimizes false predictions, reduces clinical workload, and supports intelligent healthcare decision-making. Experimental analysis demonstrates improved prediction accuracy, precision, recall, and F1-score compared with conventional machine learning approaches. The integration of IoMT and deep learning offers a scalable, secure, and efficient solution for transforming neurodegenerative disease management while promoting personalized healthcare and improving patients' quality of life.