TambuaHealth

AI-powered multi-disease prediction system

TambuaHealth is a powerful diagnostic platform that helps detect heart disease, diabetes, breast cancer, and lung cancer using patient medical data and imagery. It’s designed with simplicity and accessibility in mind, especially for under-resourced healthcare settings. With the power of machine learning and a clean, user-friendly interface, TambuaHealth brings faster, scalable, and smarter diagnosis to the forefront of modern care.

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📚 Table of Contents

• ✨ Key Features
• 🛠️ Tech Stack
• 🧠 Disease Prediction Models
• ⚙️ How It Works
• 📁 File Structure
• 📸 Screenshots
• 🚀 Future Roadmap
• 📜 License

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✨ Key Features

🛡️ Secure Access

• User Authentication: Secured registration and Login Pages.

🔍 Multi-Modal Prediction

• Manual entry or prescription scan with regex auto-fill
• Medical image upload (X-ray, CT, MRI) for deep learning inference

📊 Clinical Tools

• Instant AI-generated reports in downloadable PDF format
• Real-time feedback via React Toast notifications
• All models execute directly in the backend (Node.js only — no Flask!)

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🛠️ Tech Stack

💻 Frontend

• React (Vite) + Context API
• Tailwind CSS
• React Leaflet (for future geolocation analytics)
• React Toastify (notification system)

🧪 Backend

• Node.js + Express
• MongoDB (Atlas or Local)
• Cloudinary (image storage)
• Node.js Child Process (to call Python ML models)

🤖 Machine Learning

• Heart Disease: Logistic Regression
• Diabetes: Support Vector Machine (SVM)
• Breast Cancer: CNN
• Lung Cancer: InceptionResNet
• All models trained in Python and executed via backend integration

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🧠 Disease Prediction Models

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Condition	Model	Input Type	Key Features Scanned
Heart Disease	Logistic Regression	Form/Prescription	Age, cholesterol, BP, etc.
Diabetes	SVM	Form/Prescription	Glucose, BMI, insulin, etc.
Breast Cancer	CNN	Image Upload	Tumor shape and patterns
Lung Cancer	InceptionResNet	Image Upload	Nodule and anomaly detection

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⚙️ How It Works

1. 🔐 Welcome Page: The Welcome Page as the landing point for user authentication and access to the prediction models.
   

2. 🔐 Login: Doctor or patient signs in securely
   

3. 🧪 Choose Test: Select the disease model
   

4. 📝 Input Data:

   • Fill a form manually OR
   • Upload prescription (auto-filled via regex)
   • Upload image for cancer detection

5. ⚡ Get Results: View instant predictions + explanation
   Results are generated and displayed for user review

6. 📄 Download Report: Generate a PDF diagnosis summary
   Download your results as a PDF for your records.

# Quick start guide
cd Backend && npm install && npm run server
cd Frontend && npm install && npm run dev

🧑‍💻 About the Author

👋🏾 Hey there..

I'm Serikali, passionate student, developer dedicated to leveraging technology for impactful solutions. Interesrted in AI, software development, and innovative technologies.

🚀 Machine Learning Engineer • 🖥️ Full Stack Web Developer • 📱 Mobile App Developer

🌟 I am a lifelong learner, constantly exploring new technologies and methodologies to enhance my skills and knowledge in the field.

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🧠 Expertise & Specialties

✅ Machine Learning & Deep Learning (Sklearn, TensorFlow, PyTorch)
✅ Full Stack Web Development (React, Node.js, MongoDB)
✅ Cross-platform Mobile Apps (React Native, Flutter)
✅ Clean code, scalable systems, and beautiful UI ✨

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🌍 Let’s Connect & Collaborate

🔗 Building the future of intelligent systems—one innovation at a time!

🚀 Let’s turn ideas into reality! 📧 Email: reach out at serikalidevelopment@gmail.com for inquiries or collaboration opportunities!