Table of content
Breast-cancer Diagnostic The second greatest cause of cancer death in women, after lung cancer, is breast cancer, which is the most prevalent invasive cancer in females. Since 1989, significant progress has been made in the detection and treatment of breast cancer. More than 3.1 million Americans have survived breast cancer, according to the American Cancer Society (ACS). About 1 in 38 women will develop breast cancer in their lifetime (2.6 percent ). Early detection of the disease and precise diagnosis both increase the likelihood of long-term survival for a person with breast cancer.The prognosis, or anticipated long-term behavior of the disease, heavily influences the choice of appropriate therapy immediately following surgery.
The purpose of this project is to extract useful features by using causal inferences and building the model to predict the diagnosis.
git clone https://github.com/Micky373/causal_inference.git
cd causal_inference
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 will be here in the form of .ipynb file
All the modules for the analysis are found here
All the unit and integration tests are found here
👤 Michael Tamirie
- GitHub: Michael Tamirie
- LinkedIn: Michael Tamirie
Give a ⭐ if you like this project!