Children Hematopoietic Stem Cell Transplant Survival Status Prediction using Machine Learning
Bibliographic record
Abstract
A surgical treatment known as a bone marrow transplant (BMT) or hematopoietic stem cell transplant (HSCT) successfully treats bone marrow diseases. However, the treatment has a number of risk factors that may reduce long-term survival. This research aimed to predict the survival of children receiving Hematopoietic Stem Cell Transplantation (HSCT) or bone marrow transplant (BMT) using different machine learning classifier models, including Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), AdaBoost (AdB), Gradient Boosting Classifier (GBC), and XGBoost (XGB), using a publicly available dataset. The study involved preprocessing the data and balancing it using SMOTEENN to address the class imbalance issue. Feature selection was also performed using chi-square and correlation methods, resulting in the use of 19 out of 39 features. The data was then split into a 70–30 train-test ratio and trained using the aforementioned machine learning classifier models. The results showed that the decision tree classifier had the highest accuracy rate of 96.77%. These findings suggest that machine learning models can be utilized to predict the survival of children undergoing HSCT, with the decision tree classifier being the most accurate. So, this research provides a foundation for future studies that aim to improve the accuracy of survival predictions for children undergoing HSCT. Additionally, the findings may be utilized to aid in treatment decision-making and counseling for patients and their families.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".