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+# Health Monitoring System
+
+## Description
+<p align="justify">
+  The <b><i>Health Monitoring System</i></b> is a <b><i>web-based machine learning application</i></b> designed to predict diseases based on user-provided symptoms. The application 
+  utilizes a <b><i>Multi-Layer Perceptron (MLP Classifier)</i></b> machine learning model, achieving an <b><i>accuracy of 94.42%</i></b> and an <b><i>f1-score of 94.30%</i></b>.
+</p>
+<p align="justify">
+  This system enables users to select from <b><i>376 symptoms</i></b> and predicts up to <b><i>41 diseases</i></b> 
+  with a <b><i>confidence score threshold of 50%</i></b>, ensuring reliable and data-driven health assessments. 
+  Once a disease is predicted, the application provides detailed information including:
+</p>
+
+  1. Disease Description
+  2. Precautions
+  3. Recommended Diet
+  4. Medications
+  5. Doctor to be Consulted
+
+<p align="justify">
+  Additionally, users can download a neatly formatted PDF prescription for their records. The frontend is built using React.js, providing 
+  a seamless and interactive user experience, while the backend is powered by Flask, ensuring robust and efficient data handling.
+</p>
+
+  > [!NOTE]  
+  > Checkout the screenshots of the web app in the **[HMS-Interface Preview](./HMS-Interface%20Preview)** folder.
+
+---
+
+## Dataset
+  The dataset used in this project is a combination of two publicly available datasets:
+  1. [Disease-Symptom Dataset (Kaggle)](https://www.kaggle.com/datasets/dhivyeshrk/diseases-and-symptoms-dataset)
+  2. [Symptom-Disease Prediction Dataset (Mendeley)](https://data.mendeley.com/datasets/dv5z3v2xyd/1)
+<p align="justify">
+  After integrating these datasets, extensive processing - including symptom mapping, disease mapping, elimination of 
+  redundant features, and removal of duplicate records... was performed. The final dataset consists of:
+</p>
+
+  - **19,012 records**
+  - **376 unique symptoms**
+  - **41 disease labels**
+
+<p align="justify">
+  For additional disease-related information such as <b>description, precautions, diet, medications, and consulted doctor</b>, the following resource was used:<br>
+  <a href="https://github.com/sohamvsonar/Disease-Prediction-and-Medical-Recommendation-System/tree/main/kaggle_dataset" 
+     style="text-decoration: none; font-weight: bold; color: black;">
+    Disease-Prediction-and-Medical-Recommendation-System
+  </a>
+</p>
+
+---
+
+## Installation Guide
+  <p align="justify">
+    Follow these steps to set up and run the Health Monitoring System on your local machine.
+  </p>
+  
+### Prerequisites
+  Ensure you have the following installed:
+  <p align="justify">
+    <a href="https://www.python.org/downloads">
+      <img src="https://img.shields.io/badge/Python-3.8+-blue" alt="Python 3.8+">
+    </a>  
+    <br>
+    <a href="https://nodejs.org/en">
+      <img src="https://img.shields.io/badge/Node.js-LTS-green" alt="Node.js">
+    </a>  
+    <br>
+    <a href="https://git-scm.com">
+      <img src="https://img.shields.io/badge/Git-Latest-orange" alt="Git">
+    </a>
+  </p>
+<p align="justify">
+  <b>Step 1: Clone the Repository to your local machine</b><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;1. Run the following command to clone the repository:<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>git clone https://github.com/BharathKanuri/health-monitoring-system.git</code><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;2. Navigate to the project directory:<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>cd health-monitoring-system</code>
+</p>
+<p align="justify">
+  <b>Step 2: Set Up the Backend</b><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;1. Navigate to the backend folder:<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>cd backend</code><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;2. Create a virtual environment (optional but recommended):<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>python -m venv venv</code><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;3. Activate the virtual environment:<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;• <b>On Windows:</b><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>venv\Scripts\activate</code><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;• <b>On macOS/Linux:</b><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>source venv/bin/activate</code><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;4. Install the required Python packages:<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>pip install -r requirements.txt</code><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;5. Run the Flask backend server:<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>python app.py</code>
+</p>
+
+> [!NOTE]
+> Before proceeding, please visit **[/frontend/README.md](./frontend/README.md)** for detailed instructions on setting up the React app.
+
+<p align="justify">
+  <b>Step 3: Set Up the Frontend</b><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;1. Open a new terminal window and navigate to the frontend folder:<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>cd ../frontend</code><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;2. Install the required Node.js packages:<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>npm install</code><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;3. Start the React development server:<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<code>npm start</code>
+</p>
+<p align="justify">
+  <b>Step 4: Access the Application</b><br><br>
+  &nbsp;&nbsp;&nbsp;&nbsp;Once both the backend and frontend servers are running, open your browser and<br>
+  &nbsp;&nbsp;&nbsp;&nbsp;navigate to: <code><a href="https://localhost:3000"><b>https://localhost:3000</b></a></code> to access the Health Monitoring System.
+</p>
+
+---
+
+## Project Directory Structure
+  ![Project Directory Structure](https://github.com/user-attachments/assets/b24162c5-ab97-424d-a59c-3c40a32a0c5f)
+
+---
+
+## Usage
+| **Step** | **Description** |
+|----------|-----------------|
+| **1. Select Symptoms** | Use the search box to select your symptoms from the list of 376 symptoms. |
+| **2. Submit** | Click on the _Submit_ button to get the predicted disease with a confidence score of 50% or higher. |
+| **3. View Details** | Once a disease is predicted, view the Disease Description, Precautions, Diet, Medications, and Doctor to be Consulted. |
+| **4.&nbsp;Download&nbsp;Prescription** | If desired, download a PDF prescription for your records. |
+
+---
+
+## Technologies Used
+1. **Frontend:** React.js
+2. **Backend:** Flask (Python)
+3. **Machine Learning:** Multi-Layer Perceptron Model (scikit-learn library)
+4. **PDF Generation:** jspdf & jspdf-autotable libraries
+5. **Styling:** CSS