A COMPARATIVE EVALUATION OF SOME MACHINE LEARNING TECHNIQUES FOR PREDICTING BREAST CANCER

Abstract

With over 2 million new cases in 2020, breast cancer (BC) is the most common cancer diagnosed in women worldwide. Breast cancer is becoming more common every year, so early identification and diagnosis are crucial for reducing mortality rate. Machine learning algorithms can uncover hidden patterns and insights by analyzing massive datasets, enabling scientists to make data-driven decisions and more successfully address health concerns. In this paper, a performance comparison between four supervised machine learning techniques, namely Decision tree (DT), K-nearest neighbors (KNN), Extreme gradient Boost (XGB) and logistic regression (LR) is conducted. The primary objective is to evaluate the performance in classifying data with respect to efficiency and effectiveness of each algorithm in terms of classification test accuracy, precision, and recall. XGB outperformed the other algorithms in all the metrics used for evaluation.