A Scalable and Secure Data Analytics Framework for Decentralized Autonomous Healthcare Systems using Fuzzy Logic, Blockchain Sharding, Dynamic Network Slicing, and ECC
Bibliographic record
Abstract
Healthcare systems are increasingly challenged by the complexity of managing scalable and secure data. Addressing this, the proposed novel framework integrates elliptic curve cryptography (ECC), dynamic network slicing, blockchain sharding, and fuzzy logic to enable efficient, adaptive, and secure data analytics. The framework processes healthcare data from IoT devices, wearable sensors, and patient records, ensuring real-time analytics, scalability, and robust security. By utilizing blockchain sharding for scalability, ECC for secure encryption, and fuzzy logic for decision-making, the architecture effectively overcomes the constraints of traditional systems. The results demonstrate significant improvements, including enhanced security (98%), reduced latency (10 ms), and higher scalability (5000 TPS). These advancements establish a reliable, decentralized foundation for predictive healthcare insights, resource optimization, and adaptive governance, setting a benchmark for modern healthcare data management systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".