https://github.com/emanahmed55/clinical-decision-support-system

This project implements a Clinical Decision Support System (CDSS) that leverages machine learning algorithms to assist healthcare professionals in diagnosing diseases based on patient data and symptoms.
https://github.com/emanahmed55/clinical-decision-support-system

clinical-descision-support disease machine-learning machine-learning-algorithms prognosis-prediction

Last synced: 7 months ago
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This project implements a Clinical Decision Support System (CDSS) that leverages machine learning algorithms to assist healthcare professionals in diagnosing diseases based on patient data and symptoms.

• Host: GitHub
• URL: https://github.com/emanahmed55/clinical-decision-support-system
• Owner: EmanAhmed55
• Created: 2025-02-23T18:05:53.000Z (8 months ago)
• Default Branch: master
• Last Pushed: 2025-02-23T18:21:51.000Z (8 months ago)
• Last Synced: 2025-02-23T19:24:19.405Z (8 months ago)
• Topics: clinical-descision-support, disease, machine-learning, machine-learning-algorithms, prognosis-prediction
• Homepage:
• Size: 645 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Clinical Decision Support System

This project implements a Clinical Decision Support System (CDSS) using machine learning algorithms to assist healthcare professionals in diagnosing diseases based on patient data and symptoms.

## Table of Contents

- [Installation](#installation)

- [Usage](#usage)

- [Evaluation](#evaluation)

- [Contributing](#contributing)

## Installation

To run this project, you need to have the following Python packages installed:



  
pandas

  
numpy

  
seaborn

  
matplotlib

  
scikit-learn



You can install these packages using pip:


pip install pandas numpy seaborn matplotlib scikit-learn



## Usage



  


Load the dataset


    Load the dataset containing patient data and symptoms using pandas.

  

  


Preprocess the data


    Handle missing values, perform feature scaling, and encode categorical variables if needed.

  

  


Train the Machine Learning Classifier


    Use the algorithms to classify diseases based on symptoms. The model is trained on a cleaned and preprocessed training dataset.

  

  


Evaluate the model


    Evaluate the model's performance on a test set using metrics like accuracy and F1 score.

  



## Evaluation

The performance of the model is evaluated using:



  


Accuracy: The proportion of correct predictions.

  


F1 Score: A metric that considers both precision and recall, especially useful for imbalanced datasets.



## Contributing

Contributions are welcome! If you'd like to contribute, please:



  
Fork the repository.

  
Create a new branch.

  
Make your changes.

  
Open a pull request.