Breastcancer_withoneAPI
Giridhara Rajan
Coimbatore, Tamil Nadu
- 0 Collaborators
The entire implementation has been carried out with the intel oneAPI. A logistic regression model to identify correlations between the following 9 independent variables and the class of the tumour (benign or malignant). a. Clump thickness b. Uniformity of cell size c. Uniformity of cell shape. Mar ...learn more
Project status: Published/In Market
Intel Technologies
oneAPI
Overview / Usage
The entire implementation has been carried out with the intel oneAPI. A logistic regression model to identify correlations between the following 9 independent variables and the class of the tumour (benign or malignant).
a. Clump thickness b. Uniformity of cell size c. Uniformity of cell shape. Marginal adhesion e. Single epithelial cell f. Bare Nuclei g. Bland chromatin h. Normal nucleoli i. Mitoses
Methodology / Approach
Logistic regression can identify essential predictors of breast cancer using odds ratios and generate confidence intervals that provide additional information for decision-making. Model performance depends on the radiologists' ability to accurately identify mammogram findings. Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application. The acceleration is achieved through the use of the Intel(R) oneAPI Data Analytics Library (oneDAL). Patching scikit-learn makes it a well-suited machine-learning framework for dealing with real-life problems.
Technologies Used
ML, AI, oneAPI