- Web Development Basics: Understand frontend and backend development concepts.
- Machine Learning Fundamentals: Learn supervised learning for disease prediction.
- Database Management: Familiarize yourself with SQL or NoSQL databases.
- API Development: Learn to create RESTful APIs for communication.
- Recommendation Systems: Understand how to suggest doctors based on user data.
- Authentication & Authorization: Implement user login/signup and session management.
For Backend server application - install node modules, nodemon,bcrypt,jsonwebtokens,express,mongoose,cloudinary,body-parser,cookie-parser,dotenv through the command 'npm i', make sure to change dev script to nodemon index.js to run the index.js file on any occuring changes
Make Sure to Create a config.env file in backend/config directory and add appropriate variables in order to use the app.
Essential Variables
DATABASE_URL=
PORT =
fill each filed with your info respectively
This project aims to utilize machine learning algorithms to predict diseases based on symptoms, determining the appropriate specialist needed. Additionally, it provides recommendations for doctors based on their ratings, availability, and geographical location, enhancing the accessibility and quality of healthcare services.
The following algorithms have been explored in code:
- Logistic Regression
- Decision Tree
- Random Forest
- SVM
- Naive Bayes
- K-Nearest Neighbors
- Multilayer Perceptron
- CatBoost
The dataset for this problem used with the main.ipynb script is downloaded from here:
https://www.kaggle.com/datasets/ebrahimelgazar/doctor-specialist-recommendation-system
For running an interactive demo or sharing it with others, please run main.ipynb using Jupyter Notebook or Jupyter Lab.
jupyter notebook ML_Model/main.ipynb