Disease Recognizer
Disease Recognizer takes what patients describe (their symptoms), converts that information into a format that computers can understand (numbers), and then uses machine learning techniques to find patterns and make predictions about the disease the patient might have .
Overview
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.
Features
- 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.
Direct Run
Go to this URL: https://disease-recogniser-nlp-team-ais.streamlit.app/
Repo Link
LINK -> https://github.com/Abhi2april/Disease-Recogniser
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