The objective of these predictions is to assign patients to either a benign group that is noncancerous or a malignant group that is cancerous.
This breast cancer database was obtained from Dr. Wolberg’s office at the University of Wisconsin Hospitals, Madison. Each record here contains values for different morphological and pathological features of a tumor dissected from any given patient. The class column indicates whether the patient has been characterized as the benign tumor or a malignant tumor.
Contains 700*11 rows and columns respectively
Cancer.csv - should be in same directory as BreastCancer.py
Spyder
Pandas (library)
Keras (library)
Random Forrest (RF)
Support Vector Machine (SVM)
|True Positive False Positive | |False Negative True Negative |
True Positive = Answer (Benign) - Predicted (Benign)
False Positive = Answer (Malignant) - Predicted (Benign)
False Negative = Answer (Benign) - Predicted (Malignant)
True Negative = Answer (Malignant) - Predicted (Malignant)
True Positive = 82
False Positive = 3
False Negative = 1
True Negative = 54
True Positive = 83
False Positive = 2
False Negative = 2
True Negative = 53
SVM = 97.14 % Random Forrest = 97.14 %
For having less number of false positive we should use random forrest.
Keras.ipynb and better_tumor_classifier.py are application of deep learning model on Breast Cancer dataset