Diabetes_assessment_Model

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: [email protected]
• 🔗 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.