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A Disease-Symptoms Network and a system that predicts diseases from symptoms using a decision tree classifier.

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Predict-Disease-From-Symptoms

Contributors Stargazers Followers MIT License Open Source? Yes!

Table of Contents

About the Project

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Photo by National Cancer Institute on Unsplash

Overview

  • According to a recent study carried out by BMJ quality and safety, 12 million adults who seek medical care are misdiagnosed each year in the United States.
  • The accumulation of large case loads and incomplete medical histories lead to rise in human-errors.
  • An Artificial Intelligent system can predict and diagnose a disease at a faster and more efficient way as compared to medical professionals.
  • In the following project we will be predicting diseases from symptoms using machine learning algorithms like decision tree and random forests.
  • Moreover, we will try to plot the diseases and symptoms and create a network graph to analyse the common symptoms between diseases, most common symtoms, most common diseases etc.

Built With

Dataset

Source: Disease-Symptom Knowledge Database

  • The data set consists of 148 diseases with their corresponding symptoms.
  • The data set also included the count of disease occurences.

Results

Disease-Symptoms Network

Disease-Symptoms Network

Decision Tree

  • Model Accuracy - 90.54%

  • Decision Tree

Decision Tree

Contributing

Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/amazing-feature)
  3. Commit your Changes (git commit -m 'feat: some amazing feature')
  4. Push to the Branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Linkedin

Maharsh Suryawala - Portfolio

Project Link: https://github.com/MaharshSuryawala/Predict-Disease-From-Symptoms

References

Acknowledgements

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