Comparison of Machine Learning Classification Methods for Early Detection of Diabetes
Bibliographic record
Abstract
Diabetes is a common disease and is a significant issue in developing and developed countries.Machine learning, a branch of artificial intelligence, provides various algorithms that can efficiently process and analyze medical data to make accurate predictions.This study investigates several machine learning classification approaches for diabetes prediction from medical data sets.This study uses the Pima Indians Diabetes Database for evaluation purposes.These research uses algorithms include Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANN), K-Nearest Neighbor (KNN), XGBoost (XGB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA).This research has, as the ultimate goal, a comparison and assessment of the performance of these algorithms on diabetes prediction.Among all the above-mentioned algorithms, LR is the best-performing model for precision (72.9%) as well as for accuracy (77.73%) and F1score (65.04%).The KNN model was best illustrated by recall (63.42%).The LDA model produced the maximum Area Under the Curve (AUC) value as 83.9%.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".