🩺 MediScan AI

Smart Diagnostics for a Healthier Tomorrow

MediScan AI is a web-based medical diagnostic tool powered by Machine Learning. It helps predict the likelihood of Heart Disease, Diabetes, and Parkinson's Disease based on patient input data — built to assist in early detection and better healthcare decisions.

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🚀 Features

• 🫀 Heart Disease Prediction — Analyze 13 clinical parameters to detect heart disease risk
• 🩸 Diabetes Prediction — Predict diabetes based on glucose, BMI, insulin, and more
• 🧠 Parkinson's Disease Prediction — Detect Parkinson's using voice measurement features
• 📱 Responsive Design — Works on mobile and desktop
• ⚡ Real-time Results — Instant prediction via Flask backend API

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🛠️ Tech Stack

Layer	Technology
Frontend	HTML, CSS, JavaScript
Backend	Python, Flask
ML Models	Scikit-learn, Joblib
Data Processing	NumPy

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📁 Project Structure

Mediscan-AI/
│
├── Model/
│   └── app.py               # Flask backend with prediction routes
│
├── index.html               # Home page
├── Heart.html               # Heart disease form
├── Diabetes.html            # Diabetes form
├── Parkinson.html           # Parkinson's form
│
├── style.css                # Main stylesheet
├── heart.css                # Heart page styles
├── diabetes.css             # Diabetes page styles
├── parkinson.css            # Parkinson page styles
│
├── requirements.txt         # Python dependencies
└── run.sh                   # Shell script to run the app

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⚙️ Installation & Setup

1. Clone the Repository

git clone https://github.com/your-username/Mediscan-AI.git
cd Mediscan-AI

2. Create a Virtual Environment

python -m venv env
source env/bin/activate        # On Windows: env\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the App

bash run.sh

Or manually:

cd Model
python app.py

5. Open in Browser

http://localhost:5000

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📦 Requirements

Flask>=3.1,<4
joblib>=1.4
numpy>=2.0
scikit-learn==1.5.2

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🔬 How It Works

1. User visits the homepage and selects a disease category
2. Fills in the diagnostic form with medical parameters
3. Data is sent to the Flask backend via a POST request
4. The ML model processes the input and returns a prediction
5. Result is displayed instantly on the page

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🌐 API Endpoints

Endpoint	Method	Description
/predict-heart	POST	Heart disease prediction
/predict-diabetes	POST	Diabetes prediction
/predict-parkinson	POST	Parkinson's prediction

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👨‍💻 Author

Utkarsh

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⚠️ Disclaimer

This tool is intended for educational and research purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. Always consult a qualified healthcare provider.

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