Bibliographic record
Abstract
An abnormal rise in glucose levels may lead to diabetes. Around 30 million people are diagnosed with this disease in our country. In this perspective Indian Council of Medical Research funded by Registry of People with diabetes in India have taken an initiative and come up with numerous solutions but unfortunately neither of them has taken shape. Initially, the behavior of chemical reaction between glucose with chemical agent is estimated and tracked in the region of interest via mean shift algorithm using spatial and range information. This color change is related to plasma glucose concentration (plas), diastolic blood pressure, (pres.) Triceps skin fold thickness(skin), 2_hour serum insulin(insu), Body mass index and age. These features obtained from these 768 instances are classified using Naïve Bayes Algorithm. The results are compared with our previous work, an integrated system of K means and Naïve Bayes approach in terms of sensitivity, specificity, precision, and F-measure. It is worth noticing that our integration of mean-shift clustering and classification gives promising results with an utmost accuracy rate of 99.42% even after removing nearby duplicates in predefined clusters.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".