Implementation of Machine Learning Algorithms for Breast Cancer Detection

Abstract

Breast cancer ranks as the second most prevalent cancer in women. Detection at an early stage can save many lives. Different machine learning (ML) algorithms can be extremely useful for predicting breast cancer. The Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gaussian Nave Bayes (GaussianNB) ML algorithms were employed to predict breast cancer using the Wisconsin breast cancer dataset. The accuracy, precision, F1 score, and area under the curve (AUC score) of the receiver operating characteristics (ROC) curve were used to evaluate and compare the performance of different ML classifiers. GaussianNB had the lowest accuracy, at 95.74 percentage, while LightGBM had the most accuracy, at 98.40 percentage.