Breast Cancer Classifier using Support Vector Machine

Breast cancer


Breast cancer is the most common cancer among women in the world. It accounts for 25% of all cancer cases and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area.

Early diagnosis significantly increases the chances of survival. The key challenge against its detection is how to classify tumors into malignant (cancerous) or benign(non-cancerous). A tumor is considered malignant if the cells can grow into surrounding tissues or spread to distant areas of the body. A benign tumor does not invade nearby tissue nor spread to other parts of the body the way cancerous tumors can. But benign tumors can be serious if they press on vital structures such as blood vessels or nerves.

Machine Learning techniques can dramatically improve the level of diagnosis of breast cancer. Research shows that experienced physicians can detect cancer with 79% accuracy, while a 91 %( sometimes up to 97%) accuracy can be achieved using Machine Learning techniques.

Project Description

Github Link

In this project, my task is to classify tumors into malignant (cancerous) or benign (non-cancerous) using features obtained from several cell images.

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe the characteristics of the cell nuclei present in the image.

importing necessary libraries

I have also prepared the dataset for the model. You can view it on my GitHub!

What is SVM?

A Support Vector Machine (SVM) is a binary linear classification whose decision boundary is explicitly constructed to minimize generalization error. It is a very powerful and versatile Machine Learning model, capable of performing linear or nonlinear classification, regression and even outlier detection.

SVM is well suited for classification of complex but small or medium sized datasets.

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