Firefly Optimization Based Noise Additive Privacy-Preserving Data Classification Technique to Predict Chronic Kidney Disease
Bibliographic record
Abstract
With the continuous advancements in Information and Communication Technology, healthcare data is stored in the electronic forms and accessed remotely according to the requirements. However, there is a negative impact like unauthorized access, misuse, stealing of the data, which violates the privacy concern of patients. Sensitive information, if not protected, can become the basis for linkage attacks. Paper proposes an improved Privacy-Preserving Data Classification System for Chronic Kidney Disease dataset. Focus of the work is to predict the disease of patients’ while preventing the privacy breach of their sensitive information. To accomplish this goal, a metaheuristic Firefly Optimization Algorithm (FOA) is deployed for random noise generation (instead of fixed noise) and this noise is added to the least significant bits of sensitive data. Then, random forest classifier is applied on both original and perturbed dataset to predict the disease. Even after perturbation, technique preserves required significance of prediction results by maintaining the balance between utility and security of data. In order to validate the results, proposed method is compared with the existing technology on the basis of various evaluation parameters. Results show that proposed technique is suitable for healthcare applications where both privacy protection and accurate prediction are necessary conditions.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".