Breast Cancer Diagnosis via Classification Algorithms

TL;DR

This paper compares various machine learning classifiers on the Wisconsin Diagnostic Breast Cancer dataset, finding SVM performs best, with Bayesian Logistic Regression closely competing.

Contribution

It provides a comparative analysis of SVM, Bayesian Logistic Regression, and K-Nearest-Neighbors for breast cancer diagnosis.

Findings

SVM outperforms other classifiers on the dataset.

Bayesian Logistic Regression is a strong competitor.

K-Nearest-Neighbors has comparatively lower performance.

Abstract

In this paper, we analyze the Wisconsin Diagnostic Breast Cancer Data using Machine Learning classification techniques, such as the SVM, Bayesian Logistic Regression (Variational Approximation), and K-Nearest-Neighbors. We describe each model, and compare their performance through different measures. We conclude that SVM has the best performance among all other classifiers, while it competes closely with the Bayesian Logistic Regression that is ranked second best method for this dataset.