Deep Learning and Optimization Approaches in Securing Healthcare Data with Quaternion-Based Evolutionary Gravitational Neocognitron Neural Networks and Encoder-Elliptic Curve Deep Neural Networks Integrated with Blockchain: A Review
Bibliographic record
Abstract
The rapid expansion of digital health records, wearable devices, Internet of Medical Things (IoMT) platforms, and cloud-based healthcare systems has resulted in a massive increase in sensitive patient data being generated, stored, and transmitted across interconnected networks. This growth has simultaneously heightened vulnerability to sophisticated cyber threats, necessitating the development of secure, scalable, and intelligent data protection frameworks. This review explores advanced approaches that integrate deep learning architectures—namely Quaternion-Based Evolutionary Gravitational Neocognitron Neural Networks (QEGNN) and Encoder-Elliptic Curve Deep Neural Networks (EEC-DNN)—with blockchain technology to enhance healthcare data security. Quaternion neural networks extend traditional models into a hypercomplex domain, enabling efficient representation of multidimensional medical data such as ECG signals, MRI images, and genomic information. Evolutionary gravitational optimization improves model performance by efficiently tuning parameters and enhancing convergence. The EEC-DNN framework incorporates elliptic curve cryptography into neural encoding layers, ensuring secure and tamper-resistant data representations through embedded key exchange mechanisms. Blockchain technology further strengthens the system by providing decentralized, immutable, and transparent data management, enabling secure storage, traceability, and auditability of electronic health records. The integrated framework demonstrates strong performance across applications including medical image protection, federated learning, health record validation, and remote monitoring. Overall, this unified approach significantly enhances data security, integrity, and intelligent processing capabilities in modern healthcare environments.
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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 | 0.001 |
| 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 | 0.001 |
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".