Diabetes Risk Prediction Model Using Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Diabetes is a major global health challenge, contributing to increased mortality and long-term complications worldwide. Early diagnosis and effective risk stratification are critical to reducing the disease burden. This research aims to evaluate and compare the predictive performance of five machine learning (ML) models—Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM)—using the Pima Indians Diabetes Dataset. A standardized experimental workflow involving data preprocessing, missing value imputation, feature scaling, model training was applied. Performance metrics such as accuracy, precision, recall, F1 score, and area under the ROC curve (AUC) were used to evaluate model outcomes. Among the models tested, Gradient Boosting achieved the highest accuracy (75.97%), whereas Random Forest attained the highest AUC (0.833), indicating its superior classification capability. These results demonstrate that Random Forest model, offers a promising and practical approach for implementing robust diabetes risk prediction tools in clinical or public health contexts.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it