Breast Cancer Classifier using Logistic Regression

This code helps you classify malignant and benign tumors using Logistic Regression

Code Requirements

The example code is in Matlab (R2016 or higher will work).

You can install Conda for python which resolves all the dependencies for machine learning.

Description

Logistic regression is named for the function used at the core of the method, the logistic function.

The logistic function, also called the sigmoid function was developed by statisticians to describe properties of population growth in ecology, rising quickly and maxing out at the carrying capacity of the environment. It’s an S-shaped curve that can take any real-valued number and map it into a value between 0 and 1, but never exactly at those limits.

1 / (1 + e^-value)

For more information, see

Some Notes

1. Dataset- UCI-ML
2. I have used only 2 features out of 32 to classify.

Workign Example

Execution

To run the code, type run breast_cancer.m

run breast_cancer.m

Python Implementation

1. Dataset- UCI-ML
2. I have used 30 features to classify
3. Instead of 0=benign and 1=malignant, I have used 1=benign and 2=malignant

Acuracy ~ 92%

Execution

To run the code, type python B_Cancer.py

python B_Cancer.py