Predicting Diabetes Status Using Deep Learning
Bibliographic record
Abstract
One of the most severe public health issues affecting the global population today is that of diabetes. Diabetes prediction has remained the challenging aspect of managing this illness with the need for timely diagnosis and treatment. This study presents the classification of diabetes status based on the BRFSS2015 data using the Multi-Layer Perceptron (MLP) model. The results revealed that MLP can classify diabetes with 74.1 % accuracy and AVC of 82.2%. The variable relevance chart showed that the most highly relevant variables for predicting diabetes are physiological factors like BMI, self-reported physical health status, self-reported mental health status, blood pressure, income, age, etc. On the other hand, smoking, eating vegetables, exercising, and eating fruits are considered less necessary for predicting the disease. The reverse holds for the different lifestyle changes, such as diet and physical exercise, which made it clear that people were at risk factor for diabetes through physiological parameters. This indicates difficulty in classifying diabetes risk factors and the need for adequate diagnostic measures. Such an approach will allow for avoiding unnecessary treatment expenses and increasing people's productivity. Future research should look into how the individual variables interact with one another to enhance the predictive models and derive pertinent public health interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".