Optimized Feature Selection And Machine Learning Techniques for Early Detection of Chronic Kidney Disease
Bibliographic record
Abstract
This paper presents a comprehensive approach to early CKD detection by integrating optimal feature selection with strong machine learning. The study highlights data quality and model preparation by addressing missing values, outliers, and categorical encoding using the Kidney disease.csv dataset, which contains 26 demographic and clinical factors across 400 patient records. In order to enhance the model's performance, the optimal CKD predictors were identified using Recursive Feature Elimination (RFE) and Mutual Information Gain. In terms of accuracy and robustness, ensemble-based models—particularly Random Forest and XGBoost—bested traditional classifiers, according to an analysis of different algorithms, such as Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbours, Support Vector Machine, Naïve Bayes, and Voting Classifier. The reliability and generalisability of these models were demonstrated by their near-perfect performance in cross-validation. In order to improve patient outcomes and medical intervention, this research demonstrates that intelligent feature selection and machine learning may be used to construct diagnostic tools for early CKD detection that are both efficient and interpretable.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".