Development of predictive model of diabetic using supervised machine learning classification algorithm of ensemble voting
Bibliographic record
Abstract
Predicting the health status of patients suffering from diabetic is an important task in the health sector because the medical history of diabetic evidenced that it is a slow killer. If data collection is enough, suitable, and noise-free, such difficulties can be predicted accurately. AI-based machine learning algorithms can predict diabetes. Overfitting and underfitting impair the accuracy of classification machine learning models. Individual machine-learning models are weak learners. Hence, the demand is to develop a strong model (overall model) by combining all weak learner models to improve accuracy. Voting creates a robust and accurate model. Voting is classified as soft and hard. Ensemble machines learning models like RF, AdaBoost, and Gboost are integrated with LR, DT and KNN models. Our ensemble voting classifier model combines RF, AdaBoost, Gboost, LR, DT, and KNN. This voting model predicts diabetes with 97+ % accuracy. LR, DT, and KNN models estimate precision, recall, and F1. We tested our proposed models on two sets of input datasets with numerical and categorical features and found that categorical features improve prediction accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".