Diabetes Risk Assessment Model
A machine learning model designed to assess diabetes risk based on clinical and lifestyle features. Built as part of bridging pharmacology and ML engineering.
🎯 Purpose
Provides an accessible diabetes risk assessment tool to support early identification of at-risk individuals — particularly relevant in regions with limited access to clinical screening.
Features Used
- Age
- BMI
- Blood glucose levels
- Blood pressure
- Family history
- Physical activity
Tech Stack
- Language: Python
- Libraries: [scikit-learn / TensorFlow / PyTorch — pick what you used]
- Deployment: HuggingFace Hub
- Training data: [Dataset source — e.g., Pima Indians Diabetes Dataset, custom dataset]
📊 Performance
- Accuracy: 80%
- Precision: 75%
- Recall: 78%
- F1 Score: 85%
🚀 Usage
from huggingface_hub import hf_hub_download
import joblib
model_path = hf_hub_download(
repo_id="Oduwo/Diabetes_assessment_Model",
filename="model.pkl"
)
model = joblib.load(model_path)
# Example prediction
prediction = model.predict([[input_features]])
👨💻 Author
Emmanuel Bain Oduwo
Healthcare AI/ML Engineer | B.Pharm Student
Building AI tools at the intersection of clinical medicine and machine learning.
- 📧 Email: bainemmanuel86@gmail.com
- 🔗 GitHub: emmanuel-bain-oduwo
- 🌐 More projects: PharmaCliniq Pro, DrugD, Kemirix
📜 License
MIT License — free to use for research and educational purposes.
⚠️ Disclaimer
This model is for educational and research purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. Always consult qualified healthcare providers for medical decisions.