A High Accuracy Integrated Bagging-Fuzzy-GBDT Prediction Algorithm for Heart Disease Diagnosis
Bibliographic record
Abstract
Associated with high morbidity and mortality, heart disease has become a severe threat to peoples health throughout the world. The recent development of Internet of Things (IoT) and machine learning in e-healthcare have contributed to the monitoring, prediction and diagnosis of heart disease. Particularly, the heart disease prediction can effectively facilitate disease prevention, diagnosis and timely treatment. However, traditional prediction models are weak in accuracy and generalization. In this paper, we propose a high accuracy integrated prediction algorithm for heart disease diagnosis. The fuzzy logic and Bootstrap Aggregating (Bagging) algorithm based on Gradient Boosting Decision Tree (GBDT) algorithm are combined to process heart disease data and generate multiple weak classifiers. At first, we integrate the fuzzy logic with GBDT to reduce the complexity of data. Moreover, we develop the Fuzzy-GBDT model integrated Bagging algorithm to avoid the interference of sensitive points and achieve partial parallelism. The simulation results show the proposed Fuzzy-Bagging-GBDT algorithm improves the accuracy and recall of heart disease prediction compared with GBDT.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".