Diabetes Mellitus Prediction Using Multi-objective Genetic Programming and Majority Voting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Diabetes is one of the most serious diseases which is becoming increasingly common in recent years. Diabetes can be treated and its consequences are prevented or delayed if predicted timely. This paper investigates an evolutionary computation approach for diabetes prediction. By utilizing the multi-objective Genetic Programming Symbolic Regression, the prediction accuracy level of 79.17% is achieved. Two utilized objectives are namely prediction accuracy and complexity level of the created model (i.e., formula). Moreover, a majority-voting scheme is proposed and compared with other conventional classification algorithms. A widely studied dataset for diabetes prediction, the Pima Indian Diabetes dataset shared in University of California Irvine dataset repository, has been selected for conducting our experimental studies. The work presented here has profound implications for future applications of diabetes prediction and may one help to solve the problem of diabetes by their timely prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it