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--- a/README.md +++ b/README.md @@ -1,137 +1,137 @@ -# 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> - 1. Run the following command to clone the repository:<br> - <code>git clone https://github.com/BharathKanuri/health-monitoring-system.git</code><br><br> - 2. Navigate to the project directory:<br> - <code>cd health-monitoring-system</code> -</p> -<p align="justify"> - <b>Step 2: Set Up the Backend</b><br><br> - 1. Navigate to the backend folder:<br> - <code>cd backend</code><br><br> - 2. Create a virtual environment (optional but recommended):<br> - <code>python -m venv venv</code><br><br> - 3. Activate the virtual environment:<br> - • <b>On Windows:</b><br> - <code>venv\Scripts\activate</code><br><br> - • <b>On macOS/Linux:</b><br> - <code>source venv/bin/activate</code><br><br> - 4. Install the required Python packages:<br> - <code>pip install -r requirements.txt</code><br><br> - 5. Run the Flask backend server:<br> - <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> - 1. Open a new terminal window and navigate to the frontend folder:<br> - <code>cd ../frontend</code><br><br> - 2. Install the required Node.js packages:<br> - <code>npm install</code><br><br> - 3. Start the React development server:<br> - <code>npm start</code> -</p> -<p align="justify"> - <b>Step 4: Access the Application</b><br><br> - Once both the backend and frontend servers are running, open your browser and<br> - 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 -  - ---- - -## 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. Download 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 +# 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> + 1. Run the following command to clone the repository:<br> + <code>git clone https://github.com/BharathKanuri/health-monitoring-system.git</code><br><br> + 2. Navigate to the project directory:<br> + <code>cd health-monitoring-system</code> +</p> +<p align="justify"> + <b>Step 2: Set Up the Backend</b><br><br> + 1. Navigate to the backend folder:<br> + <code>cd backend</code><br><br> + 2. Create a virtual environment (optional but recommended):<br> + <code>python -m venv venv</code><br><br> + 3. Activate the virtual environment:<br> + • <b>On Windows:</b><br> + <code>venv\Scripts\activate</code><br><br> + • <b>On macOS/Linux:</b><br> + <code>source venv/bin/activate</code><br><br> + 4. Install the required Python packages:<br> + <code>pip install -r requirements.txt</code><br><br> + 5. Run the Flask backend server:<br> + <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> + 1. Open a new terminal window and navigate to the frontend folder:<br> + <code>cd ../frontend</code><br><br> + 2. Install the required Node.js packages:<br> + <code>npm install</code><br><br> + 3. Start the React development server:<br> + <code>npm start</code> +</p> +<p align="justify"> + <b>Step 4: Access the Application</b><br><br> + Once both the backend and frontend servers are running, open your browser and<br> + 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 +  + +--- + +## 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. Download 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