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