/breast_cancer_diagnosis

Python implementation to the paper "Evaluation of computationally intelligent techniques for breast cancer diagnosis" published on Neural Computing and Aplications journal (2021).

breast_cancer_diagnosis

Python Lint

Python implementation to the paper "Evaluation of computationally intelligent techniques for breast cancer diagnosis" published on Neural Computing and Aplications journal (2021).

Install

  • Install pipenv
  • Install dependencies using pipenv: pipenv install --skip-lock (it is important to always use the flag --skip-lock because pipenv has a problem which can take a lot of time to install if not used)
  • To access the virtual environment created, run pipenv shell, now all commands which you run will be performed into virtual enviroment created
  • Activate pre-commit hooks to use black formatter, flake8 lint and Isort references. Run pre-commit install. Now every time you make a commit, black formatter, flake8 and isort will make tests to verify if your code is following the patterns (you can adapt your IDE or text editor to follow this patterns, e.g. vs code)

Dataset

  • Instructions to download the dataset

Training ML models

  • Instructions to execute the training of ML models

Running

  • Inside the virtual environment, execute the test server using python SCRIPT_NAME_HERE