SMOTE-ENN Resampling to Optimize Diabetes Prediction in Imbalanced Data
Bibliographic record
Abstract
This study reveals the role of resampling techniques in enhancing the performance of various ML models to detect Diabetes Mellitus (DM) in imbalanced datasets, namely BRFSS 2015 and PIMA Indian.For this purpose, five ML algorithms, KNN, RF, SVM, GB, and XGB, were implemented on both datasets before and after resampling using SMOTE, SMOTE-TOMEK, and SMOTE-ENN.The results uncovered that all resampling techniques improve the performance of all ML models.The best improvement was achieved by combining the KNN model and SMOTE-ENN technique, with an accuracy of 0.97 and other evaluation metrics of 0.96-0.99 for the BRFSS 2015.For the PIMA Indian, the combination performs perfectly with all evaluation metrics, with a value of 1.0.This study observed that resampling improves the correlation between each feature and the target, making it easier for the model to recognize data patterns.It was also found that in unbalanced datasets, the role of resampling is more worthy of attention than the choice of the algorithm.With the proper technique, namely SMOTE-ENN, the difference in performance between ML models was only a maximum of 2 to 4%.It makes the ML DM prediction more flexible when applied to the health sector.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".