Chronic kidney Disease Classification through Hybrid Feature Selection and Ensemble Deep Learning
Bibliographic record
Abstract
Diagnosing and treating at-risk patients for chronic kidney disease (CKD) relies heavily on accurately classifying the disease. The use of deep learning models in healthcare research is receiving much interest due to recent developments in the field. CKD has many features; however, only some features contribute weightage for the classification task. Therefore, it is required to eliminate the irrelevant feature before applying the classification task. This paper proposed a hybrid feature selection method by combining the two feature selection techniques: the Boruta and the Recursive Feature Elimination (RFE) method. The features are ranked according to their importance for CKD classification using the Boruta algorithm and refined feature set using the RFE, which recursively eliminates the least important features. The hybrid feature selection method removes the feature with a low recursive score. Later, selected features are given input to the proposed ensemble deep learning method for classification. The experimental ensemble deep learning model with feature selection is compared to Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) models with and without feature selection. When feature selection is used, the ensemble model improves accuracy by 2%. Experimental results found that these features, age, pus cell clumps, bacteria, and coronary artery disease, do not contribute much to accurate classification tasks. Accuracy, precision, and recall are used to evaluate the ensemble deep learning model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 teacher head, 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".