Comparative Breast Cancer Detection with Artificial Neural Networks and Machine Learning Methods

Abstract

The number of cancer patients is increasing all over the world due to reasons such as environmental pollution, excess of technological and biological wastes, climate change, and increased consumption of unnatural foods. According to the data of the World Health Organization in 2020, there are over 19 million cancer patients in the world. The most common type of cancer is breast cancer with 11.7%. Breast cancer is as high as 24.5% in women and 15.5% of these cases result in death. Therefore, it is important to detect cancer at an early stage. The purpose of this study is to detect cancer in the fastest way and with the highest accuracy rates. In the study, high performance results were obtained by comparing artificial neural networks and traditional machine learning methods. In the study; Logistic Regression (LR), k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Xgboost (XGB) and Artificial Neural Network (ANN) were used. The highest result obtained using traditional machine learning methods is 98.08% with LR. In addition, using ANN, the highest value was obtained with an accuracy rate of 99.36%.