Designing an application for Chronic kidney disease (CKD) prediction
Bibliographic record
Abstract
Chronic Kidney Disease (CKD) has emerged as a major global health challenge, contributing significantly to illness, mortality, and healthcare costs. Early-stage CKD often remains undiagnosed because symptoms appear late, increasing the likelihood of patients progressing to end-stage renal failure. To address this issue, we propose a machine-learning-based CKD prediction system built using a hybrid stacking ensemble. The approach integrates Random Forest, Gradient Boosting, and Logistic Regression models with an effective feature-selection mechanism to highlight essential clinical parameters such as serum creatinine, glomerular filtration rate (GFR), and protein levels in urine.Using a comprehensive dataset containing demographic and clinical records, the model demonstrates high prediction performance. A user-friendly web interface further enables accessible, real-time CKD risk evaluation for healthcare practitioners and patients. Achieving 92% accuracy and a 95% ROC-AUC score, the proposed framework shows significant promise as a supportive tool in modern healthcare, contributing to precision-based diagnosis and early intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.003 |
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".