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. BreastCancer.py & Keras.ipynb [ Deep Learning ]

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

SVM False Positive count i.e. predicted as having benign tumor but actually have malignant tumor = 3

Random Forrest (RF)

True Positive = 83
False Positive = 2
False Negative = 2
True Negative = 53
RF False Positive count i.e. predicted as having benign tumor but actually have malignant tumor = 2

Accuracy

SVM = 97.14 % Random Forrest = 97.14 %

For having less number of false positive we should use random forrest i.e. 2

Deep Learning Model

Keras.ipynb is application of deep learning model on Breast Cancer dataset