Modern healthcare neglects the profound holistic insights of Ayurveda, leading to delayed interventions and incomplete disease predictions, demanding an integrated approach that harmonizes ancient wisdom with contemporary technology for a healthier future.
- Ayurvedic insights untapped: Ayurveda offers valuable holistic wisdom that can complement modern medicine.
- Lack of holistic disease prediction tools: Existing models often focus on symptoms without considering holistic approaches.
- Delayed interventions: Late disease detection due to limited symptom-based predictions.
AyurAI addresses the healthcare challenges by providing:
- Ayurvedic education: Educate users about the Ayurvedic remedies they may already have at home but are unaware of their uses for specific diseases, promoting self-care and holistic well-being.
- Combining AI and Ayurveda: Develop an integrated model that harnesses AI/ML for symptom-based predictions and Ayurvedic knowledge for holistic solutions.
- User-friendly platform: Create a web application using Python and Django, offering a user-friendly interface.
- Personalized herbal recommendations: Provide personalized herbal remedies based on predicted diseases, enhancing user engagement and well-being.
AyurAI utilizes the following technologies:
- Python: Use Python for implementing machine learning and deep learning algorithms.
- Django: Employ Django for building the backend and creating a robust web interface.
- HTML/CSS/JavaScript: Utilize HTML, CSS, and JavaScript for designing the user-friendly frontend.
- AI and ML: Implement AI and ML techniques for accurate symptom-based disease prediction.
- Ayurvedic Knowledge: Integrate Ayurvedic knowledge databases to enhance holistic recommendations.
AyurAI stands out by:
- Bridging ancient wisdom with modern technology: Uniquely integrates traditional Ayurvedic knowledge with cutting-edge AI/ML, providing holistic healthcare solutions.
- Personalized recommendations: Offers personalized herbal remedies based on individual symptoms, addressing the specific needs of each user.
- Holistic healthcare approach: Promotes holistic well-being by considering physical, mental, and emotional aspects, setting us apart from conventional symptom-based prediction models.
AyurAI is structured as follows:
- Frontend: Showcase the HTML/CSS interface for users to input symptoms and receive recommendations.
- Backend: Explain how Django handles server-side logic and API requests for disease prediction and Ayurvedic suggestions.
- Machine Learning: Outline the use of Python and libraries like scikit-learn for symptom-based disease prediction.
- Deep Learning: Highlight the incorporation of neural networks to enhance prediction accuracy.
- AI Integration: Emphasize how AI connects ML predictions with Ayurvedic databases to provide comprehensive recommendations.
- Clone this repository.
- Install the required dependencies.
- Run the application locally or deploy it on a web server.
This project is licensed under the MIT License