Survival Prediction of Heart Failure Patients using Stacked Ensemble Machine Learning Algorithm
Bibliographic record
Abstract
Heart failure (HF) is the leading cause of global death from chronic diseases. Data mining using machine learning (ML) converts massive volumes of raw data created by healthcare institutions into meaningful information that can aid in making predictions and crucial decisions. After an HF, collecting and analyzing follow-up data from patients is critical to monitor their health recovery. The aim of this study is to use ML and predict the survival possibility of patients after HF based on the follow-up data. Three supervised classifiers i.e., Random Forest (RF), XGBoost (XGB), and Decision Tree (DT) have been used in our study. Moreover, we proposed to design a supervised stacked ensemble learning model that can achieve a prediction accuracy, precision, recall, and F1 score of 99.98%, 100%, 99.98%, and 99.98%, respectively. The ensemble structure has three base learners (DT, RF & XGB) and one meta learner (RF). The integration of multiple algorithms boosted the prediction performance, which is a significant improvement from the other contemporary studies. Our results demonstrate that Ensemble ML can be a powerful intervention tool to predict accurately, beforehand, the recovery status of a chronic HF patient and prevent potential fatalities.
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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".