Prediction of Chronic Kidney Disease with Machine Learning Models and Feature Analysis Using SHAP
Bibliographic record
Abstract
The world is significantly impacted by chronic kidney disease (CKD), both in terms of the health and financial costs.CKD is becoming a bigger issue globally, especially in low-and middle-income nations.According to the Global Burden of Disease Survey, 697.5 million people worldwide suffered from chronic kidney disease (CKD) in 2019.Addressing the burden of CKD requires a comprehensive approach that includes prevention, earlydetection, and effective management of the condition.The main objective of this research work is to utilize Machine Learning methodologies to facilitate the diagnosis of Chronic Kidney Disease (CKD) by leveraging relevant clinical details.To accomplish this, the classification models Logistic Regression, Random Forest, Voting Classifier and Support Vector Machine are employed to distinguish patients with CKD from those without.The evaluation shows that according to the evaluations matrices Voting Classifier with soft voting showed an average classification accuracy of 98%, f1-score of 97.4%, precision of 95%, recall of 100%.Random Forest classifier showed an average classification accuracy of 96%, f1-score of 95%, precision of 90.4%, recall of 100%.Logistic regression classifier showed an average classification accuracy of 94%, f1-score of 92.6%, precision of 86.3%, recall of 100%.Support Vector Machine classifier showed an average classification accuracy of 90%, f1-score of 88.3%, precision of 79.1%, recall of 100% and proves that Voting Classifier performed well which is immediately followed by Random Forest and then Logistic Regression.Furthermore, the SHAP (SHapley Additive exPlanations) model interpretability technique is utilized to analyze the significance of each feature in determining output.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".