A Healthcare Chatbot designed to assist users with information regarding various diseases, their symptoms, and possible consultation suggestions. Built with Python, this chatbot provides easy access to healthcare-related information through an interactive interface. It covers a wide range of diseases including common cold, fever, diabetes, depression, asthma, and more.
- Disease Information: Get detailed descriptions of diseases like common cold, fever, diabetes, depression, asthma, etc.
- Symptom Checker: Input your symptoms to receive guidance on potential conditions.
- Consultation Suggestions: Based on the symptoms, the chatbot suggests consulting with healthcare professionals.
- Interactive and User-Friendly: Provides easy-to-understand responses to user queries, helping users navigate health concerns.
- AI Integration: Uses AI to understand and respond to queries in a natural language format.
- Features
- Technologies Used
- Setup & Installation
- Usage
- Code Overview
- Future Enhancements
- Contributors
- License
- Python: Main programming language for chatbot development.
- NLTK: Natural Language Toolkit used for text processing and tokenization.
- Tkinter: Python's built-in library for developing a graphical user interface (GUI).
- JSON: Data format to store and access disease information.
- Machine Learning: Used for text classification and understanding user intent.
Ensure you have Python installed on your machine. You can download it from here.
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Clone the repository:
git clone https://github.com/your-username/healthcare-chatbot.git
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Navigate to the project directory:
cd healthcare-chatbot -
Install the required dependencies:
pip install nltk
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(Optional) Download necessary NLTK data:
import nltk nltk.download('punkt') -
Run the chatbot:
python main.py
- Start the Chatbot: Once the program runs, a GUI window will open.
- Enter Your Query: Type in a health-related question or symptom in the input box (e.g., "I have a cold").
- Receive Information: The chatbot will respond with relevant details about the condition, its symptoms, and consultation suggestions.
- Consultation Suggestions: If the symptoms match a particular disease, the bot will suggest you to consult a healthcare professional.
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Receive Information
- Support Vector Classification (SVC)
- Decision Trees
- Gaussian Naive Bayes (GNB)
- Random Forest
- XGBoost
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Disease Data:
- Stored in
diseases.json, containing various diseases, their symptoms, and consultation information.
- Stored in
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Chatbot Logic:
- The core of the chatbot is handled in
chatbot.py, where user input is processed using NLP to extract meaningful information. - It matches the input with the stored data to provide responses.
- The core of the chatbot is handled in
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GUI with Tkinter:
- The
gui.pyfile contains the code to create a user interface using Tkinter. - It includes an input box for user queries, a display area for responses, and buttons for user interaction.
- The
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Machine Learning:
- The chatbot uses basic ML techniques to classify user input and map it to the relevant disease.
- It leverages tokenization and other NLP techniques to understand and process the text.
- Expanded Disease Coverage: Adding more diseases and symptoms to improve the chatbot's knowledge base.
- Voice Integration: Implementing voice-to-text functionality for a hands-free experience.
- Improved ML Models: Enhancing the chatbot's ability to diagnose with more advanced AI models.
- Medication Suggestions: Along with consultation, suggesting over-the-counter medications for minor illnesses.
- Multi-Language Support: Implementing multi-language support to cater to non-English speaking users.
- Gaurav kumar[23bcs031] (Team Leader) - Developed the core logic.
- Akash kumar chaurasiya[23bcs010] - NLP integration.
- Babul kumar[23bcs023] - GUI interface.
- Pranav Praveen[23bcs067] - Data collector.
- Dipenshu Deep Bhat[21bcs034] (Mentor) - Additional support and guidance in the project development.
This project is licensed under the MIT License. See the LICENSE file for more details.
