Disease Recognizer uses sentence embeddings generated by the sentence-transformers/all-MiniLM-L6-v2 model to encode patient symptoms into a high-dimensional space.
Machine learning algorithms, including Logistic Regression and KMeans Clustering, are employed to classify and group symptoms, ultimately predicting the associated disease.
- Symptom Embedding: Converts text-based symptoms into embeddings using a pre-trained transformer model.
- Disease Prediction: Classifies symptoms into disease categories using Logistic Regression.
- Clustering: Groups similar symptoms using KMeans Clustering.
- Data Visualization: Visualizes the embedded symptom data using t-SNE plots.
- Interactive Prediction: Allows for real-time disease prediction based on new symptom inputs.
Go to this url -> https://disease-recogniser-nlp-team-ais.streamlit.app/
- Clone the Disease-Recogniser repository:
git clone https://github.com/Abhi2april/Disease-Recogniser- Change to the project directory:
cd Disease-Recogniser- Install the dependencies:
pip install -r requirements.txt- Enter the terminal:
streamlit run app.py