M-GWO Algorithm to Predict Risk of Silent Heart Attack of Diabetes Patients - Cardidiabetes Model
Bibliographic record
Abstract
Modern healthcare system is innovation presided of next generation.Diabetes has been considered most chronic diseases and source of cardiac arrest disease.In this paper, healthcare framework has been proposed to diagnose risk of cardiac arrest due to diabetes mellitus.Findings of around 997 patients has been taken from various sources of cardio vascular disease and diabetes.Novelty of work is to pre-process dataset using GEETN process missing value with novel imputation method, and also proposed a modified Grey Wolf Optimization (M-GWO) algorithm, applied to the task of selecting an optimal feature subset for classification purposes using different machine learning models.The accumulated comparison is based on outcomes that consist of various algorithm with various algorithm like SVM_RBF, DT, KNN, RF, MLP and proposed model.Accuracy of 99 % MCC (70.35%), and f1 score (99.16 %) that helps in early detection of patients' health condition to reduce the rate of death cases, cardiodibet framework for healthcare systems helps in providing better monitoring, communication and early diagnosis of diabetes and cardiac health of patients.The proposed method identifies the preliminary status of diabetes and cardiac vascular diseases parameters of patient through Normal, Moderate and highrisk further message send for critical cases.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".