Comparative Analysis of Machine learning Model for Diabetes Prediction
Bibliographic record
Abstract
Diabetes is arguably the worst disease on the planet. It's not simply a disease; it also increases the chance of developing many other illnesses, such as renal problems, heart attacks, and visual impairments. The most difficult thing a physician can do, whether a patient has diabetes or not, is determine how likely a patient is to get the illness in the early stages. Blood glucose, BMI, age, gender, and family history are some of the interrelated elements that contribute to these problems. A range of Machine-Learning (ML) algorithms have been employed to detect and diagnose the illness in order to stop further health problems. Comparing the machine learning models for the diabetes dataset is the main focus of this work. The PIMA dataset used in this study was acquired from the UCI Repository. There are 9 features in the dataset (768 entries): glucose, pregnancies, skin thickness, insulin, BMI, diabetes pedigree function, and outcome. Random Forest, ANN (artificial neural network), Decision Tree, KNN, and Naïve Bayes are a few of the machine learning algorithms that are implemented. Recall, f1 score, accuracy, and precision are the performance metrics used. With an accuracy of over 79% in comparison to the other predictor, the data indicates that the kNN is the most accurate.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".