Medical Disease Prediction & Recommendation 🩺

Welcome to the Medical Disease Prediction & Recommendation System! This project leverages machine learning to predict diseases based on user-input symptoms and then provides recommendations for precautions, medications, diet, and workouts. The interactive web app is built using Streamlit with an attractive, custom UI.

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Table of Contents

• Overview
• Features
• Dataset
• Installation

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Overview 🚀

The Medical Disease Prediction & Recommendation System aims to help users quickly identify potential health issues based on their symptoms. The app utilizes a Support Vector Machine (SVM) with an RBF kernel to generate predictions. In addition, it provides personalized recommendations for:

• Precautions
• Medications
• Diet Plans
• Workout Suggestions

All this is delivered through a user-friendly, interactive interface built with Streamlit.

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Features 💡

• Interactive User Interface: Designed with Streamlit and enhanced with custom CSS and a background image.
• Disease Prediction: Uses a pre-trained SVM model to predict diseases based on selected symptoms.
• Personalized Recommendations: Offers detailed advice on precautions, medications, diet, and workouts.
• Dataset-Driven Insights: Leverages a comprehensive dataset from Kaggle.
• Aesthetic Design: The UI features a visually appealing background image (embedded via base64 encoding) and modern styling.

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Dataset 📊

The dataset used for this project is sourced from Kaggle:

• Disease Symptom Description Dataset

This dataset includes detailed information about various diseases, their symptoms, descriptions, and recommendations, making it a robust resource for building a healthcare prediction system.

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Installation ⚙️

1. Clone the Repository:

   git clone https://github.com/alphatechlogics/MedicalDiseasePredictionRecommendation.git
   cd MedicalDiseasePredictionRecommendation

2. Create and Activate a Virtual Environment (Optional but Recommended)

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

3. Install Required Dependencies

   pip install -r requirements.txt

4. Download the Dataset

• Visit the Kaggle Dataset and download the files.

• Place the CSV files in the data/ folder.

• Place the Background Image

• Ensure that medicineimg.jpeg is located in the root directory of the project (this image is used for the app’s background).

5. Run the App

   streamlit run app.py