/doctor-recommendation-system

Primary LanguagePython

Doctor_Recommendation_System_Based_On_UserSymptoms

Table of Contents :

  1. Prerequisites
  2. Project Design
  3. Usage
  4. Key Features
  5. Limitations

Prerequisites

  • Python
  • Pandas
  • Streamlit
  • scikit-learn
  • streamlit components
  • pymongo

Project Design

  • disease_and_symptoms.csv: This file contains the probable symptoms of the disease.
  • doctors.csv: This file contains the doctor's information, like name. Speciality, years of experience, ratings and number of ratings received
  • specialist_and_doctor.csv: This CSV file is a mapping of predicted disease and doctors' speciality.

Usage

  1. You need first install all the necessary dependencies using pip install pandas scikit-learn pymongo streamlit_components python-dotenv

  2. Place all three CSV files in the same directory

  3. You need to create database in MongoDB and connect with the app.

  4. In app.py, replace your username and password in Mongouri

  5. To run project use the command streamlit run app.py

  6. You will be able to access doctor recommendation system in web browser at the URL (Default localhost:8501)

  7. Once the application is up select the desired symptoms from the dropdown list

  8. The application will provide you with the recommended doctors along with a menu to select appointment booking dates and a rating slider (Scale 1-5)

  9. Book an Appointment with the doctor and than provide your ratings to the doctor

Key Features

  • Dynamic and Interactive User Interface
  • Dynamic Recommendations of disease and doctors
  • Doctors are Recommended based on Ratings and Doctor's Profile
  • Appointment Booking along with the Doctor's recommendation

Limitations

  • Data used here to predict disease based on symptoms is a dummy.
  • Since the data is a dummy, we cannot completely rely on the system's recommendation.
  • It will predict the closest diseases as per the symptoms provided by users.
  • Appointment booking can be improvised