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license: apache-2.0 |
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# Explainable Acute Leukemia Mortality Predictor – Model Repository |
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This repository contains the **trained machine learning model artifacts** generated by the |
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**Explainable Acute Leukemia Mortality Predictor** Hugging Face Space. |
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It serves exclusively as a **persistent storage and versioning registry** for models developed for: |
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**Mortality risk prediction in patients with acute leukemia using structured clinical data.** |
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This repository does **not** provide training or an interactive interface. |
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--- |
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## Relationship to the Application |
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Model development, validation, and prediction occur in the companion Space: |
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**Synav/Explainable-Acute-Leukemia-Mortality-Predictor** |
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Because Hugging Face Spaces use temporary storage, trained models are automatically: |
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1. Saved |
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2. Versioned |
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3. Uploaded here |
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4. Preserved as permanent releases |
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This ensures: |
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* reproducibility |
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* auditability |
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* long-term persistence |
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* external validation capability |
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--- |
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## Model Description |
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Each stored model is: |
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* **Task:** Binary mortality prediction (Yes/No) |
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* **Algorithm:** Logistic Regression (scikit-learn) |
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* **Output:** Probability of mortality (0–1) |
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* **Explainability:** SHAP feature attribution |
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### Embedded preprocessing |
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Numeric variables |
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* median imputation |
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* standard scaling |
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Categorical variables |
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* most-frequent imputation |
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* one-hot encoding |
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All preprocessing steps are embedded within the pipeline to guarantee: |
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* identical inference behavior |
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* schema consistency |
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* zero manual preprocessing |
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--- |
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## Files Included per Release |
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Each version folder contains: |
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### model.joblib |
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Complete scikit-learn pipeline including preprocessing, feature encoding, and the trained classifier. |
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Ready for immediate inference. |
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### meta.json |
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Structured metadata including: |
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* feature schema |
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* variable types |
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* evaluation metrics |
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* ROC/PR curve data |
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* calibration statistics |
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* confusion matrix |
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* decision curve analysis |
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* validation configuration |
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These artifacts enable full reproducibility and downstream analysis. |
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## Evaluation Metrics Captured |
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Models are evaluated on held-out test data using clinical-grade performance criteria. |
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### Discrimination |
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* ROC AUC |
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* ROC curve |
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* Precision–Recall curve |
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* Average Precision |
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### Classification |
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* Sensitivity (Recall) |
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* Specificity |
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* Precision |
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* F1 score |
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* Accuracy |
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* Balanced accuracy |
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* Confusion matrix |
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### Calibration |
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* Calibration (reliability) curve |
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* Brier score |
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### Clinical Utility |
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* Decision Curve Analysis (net benefit) |
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--- |
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## Repository Structure |
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``` |
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releases/ |
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└── <version>/ |
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├── model.joblib |
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└── meta.json |
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latest/ |
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├── model.joblib |
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└── meta.json |
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README.md |
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``` |
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* **releases/<version>/** → immutable historical snapshots |
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* **latest/** → most recent validated model |
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--- |
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## Intended Use |
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These artifacts are intended for: |
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* Clinical research |
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* Risk stratification studies |
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* Independent external validation |
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* Multi-center reproducibility testing |
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* Educational and exploratory analysis |
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--- |
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## Not Intended For |
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These models: |
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* are not regulatory-approved medical devices |
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* do not replace clinician judgment |
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* should not be used for autonomous decision-making |
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* require local validation prior to clinical deployment |
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Clinical oversight is mandatory. |
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--- |
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## Loading a Model |
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```python |
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import joblib |
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model = joblib.load("model.joblib") |
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proba = model.predict_proba(X)[:, 1] |
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``` |
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No additional preprocessing is required. |
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--- |
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## Author |
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Dr. Syed Naveed |
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Hematology & Oncology |
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Sheikh Shakhbout Medical City |
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Abu Dhabi, UAE |
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--- |
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## License |
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Apache 2.0 |
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--- |
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