Predicting mortality in patients with heart failure based on machine learning approaches
Bibliographic record
Abstract
Heart failure is a disease with an extraordinarily high incidence rate and mortality among cardiovascular diseases in the world. Previous experimental studies have found that the mortality rate of heart failure reaches 10% within 30 days and 50% within half a year. Therefore, effective prediction of patient mortality plays an undeniable role in the treatment of this disease. In recent years, the prediction and classification methods of machine learning have made great contributions to various fields in the world. Therefore, inspired by this, this paper will compare the accuracy of traditional linear regression and three machine learning methods for predicting mortality in patients with heart failure. The methods used in this paper include traditional logistic regression, k-nearest neighbor classification, random forest, and decision tree. To compare the accuracy of each method more effectively, the parameter changes in each method have been fully considered, including the selection of k value in the k-nearest neighbor classification, the number of variables used for growing trees in random forests, two pruning methods in decision tree, and the two model selection methods in logistic regression. Finally, the experiment result shows that the random forest has the highest 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".