Early Prognosis of Diabetes Harnessing Physiological Data and Artificial Intelligence
Bibliographic record
Abstract
This paper presents an artificial intelligence-based early detection technique for individuals with diabetes. According to the International Diabetes Federation (2024), the number of adults with diabetes is projected to exceed 850 million by 2050. Unhealthy food habits, physical inactivity, and family history are commonly blamed for diabetes. Improperly managed diabetes can lead to life-threatening complications, including cardiovascular diseases, kidney failure, nerve damage, and vision problems. Hence, early detection of diabetes is crucial and has become a primary focus of recent research. Computer-based disease detection, powered by artificial intelligence, can play a pivotal role here. With recent advances in algorithms and artificial intelligence, these technologies have become increasingly popular across diverse fields of biomedicine and bioinformatics, leading to rapid advancements in computer-based disease diagnosis. This work investigates the Pima Indian Diabetes Dataset and demonstrates that a shallow feedforward neural network (FFNN) can predict diabetes from critical biological data, achieving 79.1% accuracy. This population-based projection measure can effectively alert individuals to be vigilant and participate in recommended health screenings.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".