Performance Analysis of Diabetes Detection Using Machine Learning Classifiers
Bibliographic record
Abstract
Diabetes is a chronic medical condition that has been causing severe public health challenges in not only Canada, but the entire world, for as long as time immemorial, impacting millions of people and putting pressure on healthcare resources. That said, conventional diagnostic procedures sometimes depend on few data points and are prone to mistakes, resulting in premature action. Additionally, the sluggish adoption of modern machine learning (ML) technologies in the healthcare industries might be due to their misunderstanding of the systems’ decision making procedures. This study purports to fill that gap by looking at various machine learning (ML) algorithms and applying them on the PIMA Indians Diabetes Dataset provided by the National Health Institute of Diabetes and Digestive and Kidney Diseases with the aim of improving the validity of diabetes prediction and diagnosis. Three types of machine learning classifiers are used: Tree-based, Function-based, and Rule-based. Results have shown that Stochastic Gradient Descent (function), Logistic Regression (function), JRip (rules) and Random Forests (trees) are among the top performing classifiers. They are judged based on different metrics, such as accuracy, precision, recall, specificity, F-1 score, MCC, and ROC area. Despite performing well in almost all of the metrics, SGD’s low recall score shows that it is not the most optimal algorithm. Given that recall score is prioritized in the context of clinical diagnostics, Random Forest emerges as a strong candidate due to its balanced performance across key metrics.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".