BreastCancer

The objective of these predictions is to assign patients to either a benign group that is noncancerous or a malignant group that is cancerous.

Dataset

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

Resources

Spyder
Pandas (library)
Keras (library)

Classification Methods

Random Forrest (RF)
Support Vector Machine (SVM)

Confusion Matrix

|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)

Support Vector Machine (SVM)

True Positive = 82
False Positive = 3
False Negative = 1
True Negative = 54

Random Forrest (RF)

True Positive = 83
False Positive = 2
False Negative = 2
True Negative = 53

Accuracy

SVM = 97.14 % Random Forrest = 97.14 %

For having less number of false positive we should use random forrest.

Deep Learning Model

Keras.ipynb and better_tumor_classifier.py are application of deep learning model on Breast Cancer dataset