Table of content
Breast Cancer Screening Breast cancer, the most common invasive cancer in females, is the second leading cause of cancer death in women, after lung cancer. One in every 38 women will develop breast cancer during their lifetime (2.6 percent ). The likelihood of long-term survival for a person with breast cancer increases with early detection and precise diagnosis. In this project, we will use causal inference to extract useful features that can be used in diagnosis prediction modeling.
git clone https://github.com/skevin-dev/Causal_inference-
jupyter notebook
pip install -r requirements.txt
Data can be found here
* Diagnosis(Malignant / benign)
* the circumference (mean of distances from the center to points on the perimeter)
* the concavity (severity of concave portions of the contour)
* points that are concave (number of concave portions of the contour)
* fractal dimension of symmetry (“coastline approximation” — 1)
* the texture (standard deviation of gray-scale values)
* Perimeter\s area
* suppleness (local variation in radius lengths)
* compactness (area2 / perimeter2 — 1.0)
All the analysis and examples of implementation can be here in the form of .ipynb file
All the modules for the analysis can be found here
👤 Shyaka Kevin
- GitHub: Shyaka Kevin
- LinkedIn: Shyaka Kevin