Breast Cancer Detection using Machine Learning Classifier Algorithms

Abstract

Breast cancer is a prevalent form of cancer among women globally, particularly in developing nations where most diagnoses occur in the later stages of the disease. It is one of the most dangerous types of cancer that affects women. Cancer.net offers personalized pathways for over 120 types of cancer and genetic diseases. Previous projects have compared machine learning algorithms using various techniques such as ensemble methods, data mining algorithms, or blood analysis. This paper aims to compare six machine learning algorithms, namely Naive Bayes, Random Forest, Artificial Neural Networks, Nearest Neighbor, Support Vector Machine, and Decision Tree on the Wisconsin Diagnostic Breast Cancer dataset (WDBC) extracted from the cancer RAW CSV provided by Indian AI Productions. The dataset was divided into a training and testing phase to implement the ML algorithms. The algorithm that produces the best results will be used to classify cancerous tumors as benign or malignant based on their shape, size, texture, and smoothness.