An Enhanced Model for Smart Healthcare by Integrating Hybrid ML, LSTM, and Blockchain
Bibliographic record
Abstract
Conventional healthcare systems are traditionally challenged by fragmented data, lack of predictive insights, and security concerns, which spouse their effectiveness and efficiency.This paper will cover these gaps by developing an integrated Smart Healthcare System leveraging the power of the Internet of Things and Artificial Intelligence processes.To that end, we have proposed a holistic model that integrates several advanced methodologies to help in enhanced disease prediction and patient monitoring, with data security and privacy protection.We further apply the Hybrid Machine Learning (ML) models specifically; Random Forest Classifier integrated with k-means clustering for the prediction of diseases.This will cluster patients according to their similarity in health characteristics and provide an accurate disease risk prediction with an accuracy of 85-90%.Accordingly, Long Short Term Memory (LSTM) networks will be used for deeper timestamp series analyses with the following input sets: predicted disease probabilities, time-stamped health monitoring data, and patient lifestyle information sets.This model is outstanding both in regard to forecasting disease progression and in detecting anomalous health events with less than a 5% false positive rate.For protection and integrity of the data, we will use an Ethereum blockchain framework with respective smart contracts.The approach will provide secure, immutable health data storage and controlled, traceable access in full compliance with the requirements of various data protection regulations, such as GDPR.What's more, differentially private computations on encrypted data samples are guaranteed by combining homomorphic encryption methods with differential privacy techniques.The former ensures that in any kind of data analysis, at the point of execution, individual patient privacy is maintained, while the latter ensures an accurate, aggregated health data insight for different scenarios.By incorporating these methods, a robust smart healthcare system would be developed, one which, other than the ability to predict and monitor the progression of a disease very precisely, was able to protect patients' data and respect privacy.The same work has far-reaching implications in achieving better patient outcomes through earlier interventions and provision of increased security to the data, apart from enhancing trust in digital solutions for healthcare.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".