IoMT driven Alzheimer’s prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".