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 snapshotslatest/ → 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
1 import joblib
2
3 model = joblib . load ( "model.joblib" )
4 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
