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medical-analysis-assistant

a web appplication to assist with heart disease prediction, skin cancer and tubercolosis detection also with a health chatbot.

https://github.com/thebugged/medical-analysis-assistant

Science Score: 57.0%

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    DOI references
    Found 1 DOI reference(s) in README
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    Scientific vocabulary similarity
    Low similarity (12.1%) to scientific vocabulary

Keywords

data-science healthcare jupyter-notebook keras machine-learning python scikit-learn streamlit tensorflow
Last synced: 6 months ago · JSON representation ·

Repository

a web appplication to assist with heart disease prediction, skin cancer and tubercolosis detection also with a health chatbot.

Basic Info
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Topics
data-science healthcare jupyter-notebook keras machine-learning python scikit-learn streamlit tensorflow
Created about 2 years ago · Last pushed 6 months ago
Metadata Files
Readme Citation

README.md


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python tensorflow keras scikit-learn

Medical Analysis Assistant

The Medical Analysis Assistant is a holistic health companion, offering predictions for heart disease, tuberculosis detection, skin cancer classification, and expert insights through a user-friendly chat interface.

Datasets 🗃️ - Heart Disease Cleveland UCI - Skin Cancer only HAM10000_images_part_1, .._part_2, and HAM10000_metadata.csv required - Tuberculosis (TB) Chest X-ray Database

Setup & Installation

Prerequisites

Ensure the following are installed - Git - Python - Jupter Notebook (or install the Jupyter extension on Visual Studio Code).

To set up this project locally, follow these steps:

  1. Clone the repository: shell git clone https://github.com/thebugged/medical-analysis-assistant.git

  2. Change into the project directory: shell cd medical-analysis-assistant

  3. Install the required dependencies: shell pip install -r requirements.txt

Running the application

  1. Run the command: shell streamlit run main.py
  2. Alternatively, you can run the heart.ipynb,tb.ipynb, and skin.ipynb notebooks to get their respective models then run the command in 1.

The application will be available in your browser at http://localhost:8501.

Streamlit App

Owner

  • Name: Maikyau Israel
  • Login: thebugged
  • Kind: user

developer developing

Citation (citation.xml) 

<?xml version='1.0' encoding='UTF-8'?><xml><records><record><ref-type name="Dataset">59</ref-type><contributors><authors><author>Tschandl, Philipp</author></authors><secondary-authors><author>ViDIR Group</author></secondary-authors></contributors><titles><title>The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions</title></titles><section>2018-06-04</section><dates><year>2018</year></dates><edition>V4</edition><keywords><keyword>Dermatoscopy</keyword></keywords><custom3>Digital dermatoscopic images</custom3><language>English</language><publisher>Harvard Dataverse</publisher><urls><related-urls><url>https://doi.org/10.7910/DVN/DBW86T</url></related-urls></urls><electronic-resource-num>doi/10.7910/DVN/DBW86T</electronic-resource-num></record></records></xml>

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Dependencies

requirements.txt pypi
  • Pillow ==9.4.0
  • joblib ==1.2.0
  • keras ==2.15.0
  • matplotlib ==3.7.1
  • numpy ==1.24.2
  • openai ==1.9.0
  • pandas ==2.1.1
  • scikit-learn ==1.4.0
  • seaborn ==0.12.2
  • split-folders ==0.5.1
  • streamlit ==1.30.0
  • streamlit_option_menu ==0.3.12
  • tensorflow ==2.15.0