Analysis and prediction of breast cancer using data from Wisconsin Diagnostic Breast Cancer (WDBC). https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29
python breast_cancer_wisconsin.py
CORRELATIONS WITH CLASS
| uniformity_cell_size | 0.817904 |
| uniformity_cell_shape | 0.818934 |
| marginal_adhesion | 0.696800 |
| single_epithelial_cell_size | 0.682785 |
| bland_chromatin | 0.756616 |
| normal_nucleoli | 0.712244 |
| mitoses | 0.423170 |
| clump_thickness | 0.716001 |
You will notice that the variable 'mitoses' has low correlation with 'Class' so I will remove it from the classifier
and below is a scatter plot of the correlations
Running a Logistic Regression model on the data to classify which records are benign (2) or malignant (4) we see the following:
The Accuracy is 0.96
Predictions Value Count
| label | count |
|---|---|
| 2 | 444 |
| 4 | 239 |
Sensitivity is 0.95 Specificity is 0.97
Average Accuracies after 10 K-Folds: 0.96