IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0.
Authors
Affiliations (6)
Affiliations (6)
- Department of Computer Science, Green International University, Lahore, Pakistan.
- School of Computing, Academic Center, Bath Spa University, Ras-al-Khaima, United Arab Emirates.
- School of Computer Science (SCS), Taylor's University, 47500, Subang Jaya, Malaysia.
- Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, 31982, Al-Ahsa, Saudi Arabia. [email protected].
- Department of Artificial Intelligence, NASTP Institute of Information Technology, Lahore, Pakistan.
- Department of Computer Science , NASTP Institute of Information Technology, Lahore, Pakistan.
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the primary cause of dementia, responsible for 60-70% of global cases. It severely affects memory, cognitive function, and daily independence, placing a substantial emotional and economic burden on patients and caregivers. Early and accurate prediction remains difficult due to the high cost of neuroimaging, scarcity of annotated datasets, and the "black-box" nature of most artificial intelligence (AI) models. With the emergence of Healthcare 5.0, the Internet of Medical Things (IoMT) offers new opportunities for patient-centric, real-time monitoring and data-driven diagnosis. This study proposes an IoMT-driven Alzheimer's prediction framework that combines transfer learning (ResNet152) with explainable AI (XAI) to provide both accuracy and interpretability. The publicly available Kaggle Alzheimer's MRI dataset, comprising 33,984 images across four classes (Non-Demented, Very Mild, Mild, and Moderate Demented) was employed. To address class imbalance, a Conditional Wasserstein GAN was applied for synthetic image generation and balanced sampling. The proposed ResNet152-TL-XAI model achieved 97.77% accuracy, with a precision of 0.981, recall of 0.987, F1-score of 0.983, and specificity of 99.13%, outperforming several state-of-the-art methods. Interpretability was ensured through Grad-CAM, SHAP, and LIME, which consistently highlighted clinically relevant brain regions such as the Hippocampus and ventricles, confirming biological plausibility and increasing clinician trust. By integrating IoMT-enabled data acquisition, transfer learning for efficient training, and multi-method XAI for transparency, the proposed pipeline demonstrates strong potential for early, accurate, and interpretable Alzheimer's staging. These results position the framework as a practical candidate for integration into Healthcare 5.0 ecosystems, supporting timely diagnosis, patient monitoring, and personalized interventions.