AYUCARE is a web application that uses machine learning algorithms to detect diseases from symptoms and recommend Ayurvedic medicine. The application uses two machine learning models, both of which use decision tree algorithms.
To run the application, you will need to install the many packages, some main one like:
- Flask
- scikit-learn
- pandas
- numpy
To install these packages, you can use the following command:
pip install -r requirements.txt
To run the application, navigate to the Samudini directory and run the following command:
python app.py
This will start the Flask development server, and the application will be accessible at http://localhost:5000.
The application uses two machine learning models to detect diseases and recommend Ayurvedic medicine:
-
Decision Tree Classifier: This model is trained on a dataset of symptoms and their corresponding diseases. Given a set of symptoms, the model predicts the most likely disease.
-
Decision Tree Classifier: This model is trained on a dataset of diseases and their corresponding Ayurvedic medicines. Given a disease, the model recommends the most effective Ayurvedic medicine.
The datasets used to train the machine learning models are available in the dataset directory of Nadun and Abdullah directory. The data.csv file contains a list of symptoms and their corresponding diseases, and the Drug prescription Dataset.csv file contains a list of diseases and their corresponding Ayurvedic medicines.
If you would like to contribute to this project, you can fork the repository and submit a pull request. Please make sure to follow the existing code style and include test cases for any new functionality.