Diagnosify - AI Disease Prediction Platform

A modern web application for disease prediction using machine learning models. The platform supports multiple disease categories and allows users to upload custom datasets for training new prediction models.

Features

• Multiple Disease Predictions: Diabetes, Heart Disease, and Parkinson's Disease
• Real Model Integration: Uses your trained .sav model files
• Dataset Upload: Upload CSV files to create new disease prediction models
• Responsive Design: Modern UI optimized for desktop and mobile
• RESTful API: Flask backend with proper error handling

Project Structure

diagnosify/
├── backend/
│   ├── app.py              # Main Flask application
│   ├── run.py              # Server startup script
│   ├── requirements.txt    # Python dependencies
│   ├── models/             # Place your .sav model files here
│   │   ├── README.md       # Model requirements and naming convention
│   │   ├── diabetes_model.sav      # (Your diabetes model)
│   │   ├── heart_model.sav         # (Your heart disease model)
│   │   └── parkinsons_model.sav    # (Your Parkinson's model)
│   └── uploads/            # Uploaded CSV files storage
└── frontend/               # React frontend
    ├── src/
    ├── public/
    ├── pages/
    ├── utils/
    └── components/

Setup Instructions

Backend Setup

1. Navigate to backend directory:

   cd backend

2. Install Python dependencies:

   pip install -r requirements.txt

3. Add your trained models:

   • Place your .sav model files in the backend/models/ directory
   • Follow the naming convention: diabetes_model.sav, heart_model.sav, parkinsons_model.sav
   • See backend/models/README.md for detailed requirements

4. Start the backend server:

   python run.py

   The backend will be available at http://localhost:5000

Frontend Setup

The frontend is already configured. Make sure the backend is running before testing predictions.

Model Requirements

Your models should be scikit-learn models saved using joblib:

import joblib
from sklearn.ensemble import RandomForestClassifier

# Train your model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Save the model
joblib.dump(model, 'diabetes_model.sav')

Expected Input Features

Diabetes Model (8 features):

• pregnancies, glucose, bloodPressure, skinThickness, insulin, bmi, diabetesPedigree, age

Heart Disease Model (13 features):

• age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal

Parkinson's Model (22 features):

• fo, fhi, flo, jitter_percent, jitter_abs, rap, ppq, ddp, shimmer, shimmer_db, apq3, apq5, apq, dda, nhr, hnr, rpde, dfa, spread1, spread2, d2, ppe

API Endpoints

• GET /health - Check backend status and loaded models
• POST /predict/<disease_type> - Make predictions
• POST /upload-dataset - Upload new datasets

Usage

1. Start the backend server following the setup instructions above
2. Access the web app in your browser
3. Select a disease category from the homepage
4. Fill in the medical parameters in the prediction form
5. Get instant predictions with confidence scores
6. Upload new datasets via the upload page for custom disease categories

Security Notes

• Models are loaded securely using joblib
• File uploads are validated and stored safely
• CORS is properly configured for frontend-backend communication
• Input validation prevents malicious data injection

Adding New Disease Categories

1. Upload a CSV dataset via the web interface
2. Train a scikit-learn model using the uploaded data
3. Save the model as a .sav file in the backend/models/ directory
4. Restart the backend server to load the new model
5. The new disease category will be automatically available

Medical Disclaimer

This tool is for educational and informational purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment.