Predictive Modelling of Glycated Hemoglobin Levels Using Machine Learning Regressors
Bibliographic record
Abstract
Diabetes is a chronic condition characterized by elevated levels of blood glucose, also known as hyperglycemia.Measurement of HbA1c is a widely used blood test that provides an essential tool for monitoring diabetic progression and assessing the effectiveness of diabetes management but this test is usually not conducted until there are some symptoms of diabetes in the patient and sometimes it goes unnoticed for a longer period resulting in the late detection of the disease.This study proposes a novel approach to HbA1c Prediction using machine learning regression algorithms on various features including Age, BMI, and hematological parameters.This study also compares the performance of ten machine learning regressors on the prediction of HbA1c level using performance metrics such as Mean square error, Root mean squared error, Mean absolute error MAE, R square, Adjusted R square, and Minimum Absolute Percentage Error.Result: Linear regression was found as the best performer with an R square and adjusted R square value of 1.00, Mean square error, Root mean squared error, Mean absolute error, and Minimum Absolute Percentage Error of 0.00.A higher HbA1c Level predicted using this method should go for actual HbA1c testing for confirmation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".