Comparative Evaluation of Machine Learning Models for Diabetes Prediction: A Focus on Ensemble Methods
Bibliographic record
Abstract
Diabetes is a persistent health condition that impacts millions of people globally.Early and accurate prediction of this disease is critical for prevention and effective management.Machine learning models have emerged as promising tools for this task; however, the variability in the performance of different algorithms requires a thorough evaluation to identify the most effective ones.The main objective of this study was to assess several machine learning models using different performance metrics to identify the most robust and consistent approaches to diabetes prediction.Nine machine learning models were evaluated using the Pima Indian dataset, with data balancing performed via Synthetic Minority Over-sampling Technique (SMOTE) and performance assessed through crossvalidation and test data.Among the models, Random Forest and AdaBoost produced the most robust and consistent results across key metrics, such as the AUC-ROC and AUPRC.These findings highlight their potential use in clinical decision support systems for early risk detection and improved patient management.In conclusion, the study emphasizes the significance of utilizing various evaluation metrics to obtain a thorough insight into the performance of machine learning models in predicting diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".