Model Card for NZR – Breast Cancer Early Detection AI
This model performs binary classification to detect malignant versus benign tumors using clinical diagnostic data from the Wisconsin Breast Cancer Diagnostic Dataset. It was built with a Random Forest classifier and designed for early-stage breast cancer screening support.
This modelcard aims to be a base template for open-sourced medical AI, particularly for structured (tabular) data inputs.
Model Details
Model Description
- Developed by: Alan Jafari (TekTonic AI)
- Funded by [optional]: Self-funded (independent research)
- Shared by: TekTonic AI
- Model type: Random Forest Classifier (100 estimators)
- Language(s) (NLP): Not applicable (structured data)
- License: MIT
- Finetuned from model: N/A (trained from scratch)
Model Sources
- Repository: [Add GitHub/Kaggle link]
- Paper [optional]: N/A
- Demo [optional]: [Add Streamlit or web demo link if available]
Uses
Direct Use
This model can be directly used to classify breast cancer tumors (benign vs malignant) using clinical feature data. It accepts 10 numerical features as input (e.g., radius_mean, texture_mean, etc.) and returns a class prediction.
Downstream Use
- Integration in medical dashboards
- Research on breast cancer ML techniques
- Educational applications in healthcare AI
Out-of-Scope Use
- Not for use with image data (e.g., mammograms or MRI)
- Not a substitute for clinical diagnosis
- Not trained for male breast cancer or pediatric cases
Bias, Risks, and Limitations
- Trained only on adult female data
- Demographic diversity not captured
- Possible overconfidence on limited data
- No interpretability module (e.g., SHAP not embedded yet)
Recommendations
- Use under expert medical supervision
- Complementary to, not a replacement for, radiology or biopsy
- Retrain with local data for domain adaptation
How to Get Started with the Model
1import joblib
2import numpy as np
3
4model = joblib.load("nzr_model.pkl")
5
6# Example input: 10 numerical diagnostic features
7x_input = np.array([[14.5, 20.0, 95.0, 660.0, 0.1, 0.15, 0.08, 0.05, 0.18, 0.06]])
8prediction = model.predict(x_input)
Training Details
Training Data
- Dataset: Wisconsin Breast Cancer Diagnostic Dataset
- Features: 10 handpicked numerical features from the full set of 30
- Labels:
0 = Benign, 1 = Malignant
Training Procedure
- Train/test split: 80/20
- Scikit-learn RandomForestClassifier with 100 trees
- Standardization and cleaning applied
Training Hyperparameters
n_estimators = 100max_depth = None- Precision:
fp32
Speeds, Sizes, Times
- Training time: <10s (on CPU)
- Inference latency: ~1ms
- Model size: ~250 KB
Evaluation
Testing Data, Factors & Metrics
Testing Data
Factors
- No demographic breakdown available
Metrics
- Accuracy: 96.5%
- Precision: 98%
- Recall: 93%
- F1-Score: 95.5%
Results
The model demonstrated high performance on classification accuracy and precision, while maintaining balance between false positives and false negatives.
Summary
Reliable early-stage diagnostic support tool for breast cancer screening based on structured input.
Model Examination
Feature importance rankings are available upon request. No black-box dependencies.
Environmental Impact
- Hardware Type: CPU (Intel i7)
- Hours used: ~0.01
- Cloud Provider: N/A (local)
- Compute Region: Iran
- Carbon Emitted: Negligible
Technical Specifications
Model Architecture and Objective
Random Forest binary classifier, built using 100 decision trees. Optimized for high recall on malignant class.
Compute Infrastructure
Hardware
- CPU: Intel Core i7 (local machine)
Software
- Python 3.9
- Scikit-learn 1.4+
Citation
BibTeX:
1@misc{nzr2025,
2 title={NZR: Breast Cancer Early Detection Model},
3 author={Alan Jafari},
4 year={2025},
5 howpublished={\url{https://huggingface.co/USERNAME/nzr-model}},
6}
APA:
Jafari, A. (2025).
NZR: Breast Cancer Early Detection Model [Machine learning model]. Hugging Face.
https://huggingface.co/USERNAME/nzr-model
Glossary
- Malignant: Cancerous, potentially dangerous
- Benign: Non-cancerous tumor
- Precision: TP / (TP + FP)
- Recall: TP / (TP + FN)
More Information
Contact via t.me/alan_jafari
Model Card Authors
- Alan Jafari (TekTonic AI)
Model Card Contact
